14 January 2025 to 20 June 2024 · tagged listen/tech
¶ What we know · 14 January 2025 listen/tech
The thing I worked on the longest, in my Echo Nest / Spotify time, was calculating artist similarity. In the Echo Nest days, when we didn't have direct listening data, we derived scores for pairs of artists based on patterns of shared descriptive words found in web pages about each of them. Or, more accurately, web pages maybe about them, because figuring out whether any given blob of text that contains a given string of letters is about a band whose name is that same string of letters, at all never mind in a descriptively useful sense, is hard.
Once we got swallowed by Spotify, of course, we had all the listening-data plankton we could krill. The goal of "collaborative filtering", taken most lowercasely, is to extract collective knowledge from collected data. The Spotify feature this work powered was called (eventually) Fans Also Like, and one of my greatest organizational triumphs at Spotify was that after many years of technical work and political lobbying, I succeeded in making this feature live up to its name. For about a year and a half, starting around April 2023, the Spotify artist page Fans Also Like lists were really an algebraic formulation of getting each artist's fans, not just listeners, and finding out what other artists they disproportionately also liked. And nothing else. You only saw the first 20 results for each artist, but the underlying dataset behind this went deeper, and I think was a genuinely unprecedented collective cultural achievement of the Spotify audience.
Most of the complexity of doing this well, if by "well" you mean reflecting actual patterns of human interest as opposed to round-off error in vector embeddings or clandestine margin-chiseling, which you should, was actually in the quantification of "fan". I would not be able to explain all the details of that process from memory, even if I were allowed to, but the core idea is that the more you raise the threshold of fandom, the better similarity signal you get from the listening patterns of those fans, but the fewer artists are included, so if you want both precision and recall, you have to get creative.
And of course you have to get data, to begin with. We cannot recreate the lost Fans Also Like network from outside of Spotify, because we cannot get their dataset of fan/artist pairs. Or, really, our dataset, because it's our listening.
If you happen to have pairs of any kind of data, though, doing simple math to extract similarity of one half of those pairs based on the co-reference patterns of the other half is easy. In fact, if you have that pair data in JSON, you can load it into the spec/doc/test/playground page for Dactal and do it right now.
For example, over at AlbumOfTheYear.org they aggregate album-of-the-year lists from many other publications and produce a scored meta-ranking of the year's albums. But this dataset of publication/album pairs also encodes patterns of implicit knowledge about album similarity based on the tendencies of publications to list albums together, and about publication similarity based on the tendencies of albums to appeal to publications together.
Here's how to extract it using Dactal:
The first orange line is specific to this data, my extraction from the AOTY lists, but all it needs to produce is a two-column list with x and y. Like this:
Once you have any x/y list like this, the rest of the query works the same. The scoring logic here (which isn't what I used at Spotify, but you probably aren't dealing with 600 million people listening to 10 million artists) counts the overlap between any pair of x based on y, and then scales that by the maximum overlaps of both parts of the pair. So a score of 1 means that both things in that pair are each other's closest match. The calculation is asymmetric because one part of a pair may be the other's closest match but not vice versa. If you read about music online you may know that, e.g., Decibel and Metal Hammer are both metal-specific, The Guardian and NME are both British, and BrooklynVegan and Stereogum are both read by the kind of people who read BrooklynVegan and Stereogum, so the top of those results passes a basic sanity check.
And because everything but the first line is independent of what x and y are, that means we can flip x and y (just those two letters!) and get album similarity:
This passes a sanity check – everybody who writes about music likes Charli – but not an interestingness check, so we might opt to filter out BRAT just to see what else we can learn:
Not bad! The two Future/Metro Boomin albums are most similar to each other, which is the good kind of confidence-boosting boring answer, but a bunch of the other pairs are plausible yet non-obvious: two indie rock records, two UK indie guitar records, two indie rappers, two metal-adjacent records.
These scores are normalized locally, not globally, so the real way to use them is to reorganize this by album. Which is also easy:
That's interesting to me. What's interesting to you?
[PS: Oh, here, I put this dataset up in raw interactive form, so you can play with it yourself if you want.]
Once we got swallowed by Spotify, of course, we had all the listening-data plankton we could krill. The goal of "collaborative filtering", taken most lowercasely, is to extract collective knowledge from collected data. The Spotify feature this work powered was called (eventually) Fans Also Like, and one of my greatest organizational triumphs at Spotify was that after many years of technical work and political lobbying, I succeeded in making this feature live up to its name. For about a year and a half, starting around April 2023, the Spotify artist page Fans Also Like lists were really an algebraic formulation of getting each artist's fans, not just listeners, and finding out what other artists they disproportionately also liked. And nothing else. You only saw the first 20 results for each artist, but the underlying dataset behind this went deeper, and I think was a genuinely unprecedented collective cultural achievement of the Spotify audience.
Most of the complexity of doing this well, if by "well" you mean reflecting actual patterns of human interest as opposed to round-off error in vector embeddings or clandestine margin-chiseling, which you should, was actually in the quantification of "fan". I would not be able to explain all the details of that process from memory, even if I were allowed to, but the core idea is that the more you raise the threshold of fandom, the better similarity signal you get from the listening patterns of those fans, but the fewer artists are included, so if you want both precision and recall, you have to get creative.
And of course you have to get data, to begin with. We cannot recreate the lost Fans Also Like network from outside of Spotify, because we cannot get their dataset of fan/artist pairs. Or, really, our dataset, because it's our listening.
If you happen to have pairs of any kind of data, though, doing simple math to extract similarity of one half of those pairs based on the co-reference patterns of the other half is easy. In fact, if you have that pair data in JSON, you can load it into the spec/doc/test/playground page for Dactal and do it right now.
For example, over at AlbumOfTheYear.org they aggregate album-of-the-year lists from many other publications and produce a scored meta-ranking of the year's albums. But this dataset of publication/album pairs also encodes patterns of implicit knowledge about album similarity based on the tendencies of publications to list albums together, and about publication similarity based on the tendencies of albums to appeal to publications together.
Here's how to extract it using Dactal:
?data=(aoty.entries.(....x=aotylist,y=albumkey))
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
#score
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
#score
The first orange line is specific to this data, my extraction from the AOTY lists, but all it needs to produce is a two-column list with x and y. Like this:
?data=(aoty.entries.(....x=aotylist,y=albumkey))
Once you have any x/y list like this, the rest of the query works the same. The scoring logic here (which isn't what I used at Spotify, but you probably aren't dealing with 600 million people listening to 10 million artists) counts the overlap between any pair of x based on y, and then scales that by the maximum overlaps of both parts of the pair. So a score of 1 means that both things in that pair are each other's closest match. The calculation is asymmetric because one part of a pair may be the other's closest match but not vice versa. If you read about music online you may know that, e.g., Decibel and Metal Hammer are both metal-specific, The Guardian and NME are both British, and BrooklynVegan and Stereogum are both read by the kind of people who read BrooklynVegan and Stereogum, so the top of those results passes a basic sanity check.
And because everything but the first line is independent of what x and y are, that means we can flip x and y (just those two letters!) and get album similarity:
?data=(aoty.entries.(....y=aotylist,x=albumkey))
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
#score
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
#score
This passes a sanity check – everybody who writes about music likes Charli – but not an interestingness check, so we might opt to filter out BRAT just to see what else we can learn:
?data=(aoty.entries.(....y=aotylist,x=albumkey))
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
:!(.x1,x2:=[Charli xcx: BRAT])
#score
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
:!(.x1,x2:=[Charli xcx: BRAT])
#score
Not bad! The two Future/Metro Boomin albums are most similar to each other, which is the good kind of confidence-boosting boring answer, but a bunch of the other pairs are plausible yet non-obvious: two indie rock records, two UK indie guitar records, two indie rappers, two metal-adjacent records.
These scores are normalized locally, not globally, so the real way to use them is to reorganize this by album. Which is also easy:
?data=(aoty.entries.(....y=aotylist,x=albumkey))
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
:!(.x1,x2:=[Charli xcx: BRAT])
#score
/iyl=(.x1,x2)
.(....iyl,yml=(._,(.of.x1,x2):@>1:@<=10)
?paircounts=(data/y/x1=(.of.x),x2=(.of.x):(.key:@2):count>=5)
?x maxpoints=(paircounts/key||maxpoints=(.of.count....max))
?paircounts
||x1max=(.x1.x maxpoints.maxpoints),
x2max=(.x2.x maxpoints.maxpoints),
score=[=2*count**2/((count+x1max)*x2max)]
:!(.x1,x2:=[Charli xcx: BRAT])
#score
/iyl=(.x1,x2)
.(....iyl,yml=(._,(.of.x1,x2):@>1:@<=10)
| 146 | iyl | yml |
| 1 | A. G. Cook: Britpop | 10 Maggie Rogers: Don't Forget Me Remi Wolf: Big Ideas Clairo: Charm Jack White: No Name Magdalena Bay: Imaginal Disk Vampire Weekend: Only God Was Above Us Fontaines D.C.: Romance Doechii: Alligator Bites Never Heal Tyler, The Creator: CHROMAKOPIA ScHoolboy Q: BLUE LIPS |
| 2 | Adrianne Lenker: Bright Future | 10 Vampire Weekend: Only God Was Above Us The Last Dinner Party: Prelude to Ecstasy The Cure: Songs of a Lost World Fontaines D.C.: Romance Arooj Aftab: Night Reign Kim Gordon: The Collective Magdalena Bay: Imaginal Disk Floating Points: Cascade Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks |
| 3 | Amyl and The Sniffers: Cartoon Darkness | 10 English Teacher: This Could Be Texas The Last Dinner Party: Prelude to Ecstasy Bob Vylan: Humble As The Sun Hamish Hawk: A Firmer Hand IDLES: TANGK SPRINTS: Letter To Self Fontaines D.C.: Romance The Cure: Songs of a Lost World Waxahatchee: Tigers Blood Wunderhorse: Midas |
| 4 | Ariana Grande: eternal sunshine | 10 Billie Eilish: HIT ME HARD AND SOFT Sabrina Carpenter: Short n' Sweet Dua Lipa: Radical Optimism Beyoncé: COWBOY CARTER Kacey Musgraves: Deeper Well Clairo: Charm Doechii: Alligator Bites Never Heal Vampire Weekend: Only God Was Above Us Magdalena Bay: Imaginal Disk The Cure: Songs of a Lost World |
| 5 | Arooj Aftab: Night Reign | 10 Adrianne Lenker: Bright Future Jessica Pratt: Here in the Pitch Beth Gibbons: Lives Outgrown Cindy Lee: Diamond Jubilee MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Nala Sinephro: Endlessness Vampire Weekend: Only God Was Above Us Waxahatchee: Tigers Blood Nilüfer Yanya: My Method Actor |
| 6 | Astrid Sonne: Great Doubt | 4 Nala Sinephro: Endlessness Nilüfer Yanya: My Method Actor MJ Lenderman: Manning Fireworks Mk.gee: Two Star & The Dream Police |
| 7 | Being Dead: EELS | 10 This Is Lorelei: Box For Buddy, Box For Star Mannequin Pussy: I Got Heaven Jessica Pratt: Here in the Pitch Nilüfer Yanya: My Method Actor Blood Incantation: Absolute Elsewhere Magdalena Bay: Imaginal Disk Mk.gee: Two Star & The Dream Police Vampire Weekend: Only God Was Above Us MJ Lenderman: Manning Fireworks Clairo: Charm |
| 8 | Beth Gibbons: Lives Outgrown | 10 The Cure: Songs of a Lost World Arooj Aftab: Night Reign Jessica Pratt: Here in the Pitch Adrianne Lenker: Bright Future Kim Gordon: The Collective Julia Holter: Something in the Room She Moves Mannequin Pussy: I Got Heaven Waxahatchee: Tigers Blood Nilüfer Yanya: My Method Actor Chat Pile: Cool World |
| 9 | Beyoncé: COWBOY CARTER | 10 Billie Eilish: HIT ME HARD AND SOFT Sabrina Carpenter: Short n' Sweet Doechii: Alligator Bites Never Heal Kendrick Lamar: GNX Jack White: No Name The Cure: Songs of a Lost World Kali Uchis: ORQUÍDEAS Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks ScHoolboy Q: BLUE LIPS |
| 10 | BigXthaPlug: TAKE CARE | 4 GloRilla: GLORIOUS Doechii: Alligator Bites Never Heal Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX |
| 11 | Bill Ryder-Jones: Iechyd Da | 10 Amyl and The Sniffers: Cartoon Darkness Arooj Aftab: Night Reign Fontaines D.C.: Romance Adrianne Lenker: Bright Future The Cure: Songs of a Lost World Kim Gordon: The Collective Beth Gibbons: Lives Outgrown The Last Dinner Party: Prelude to Ecstasy Vampire Weekend: Only God Was Above Us Cindy Lee: Diamond Jubilee |
| 12 | Billie Eilish: HIT ME HARD AND SOFT | 10 Beyoncé: COWBOY CARTER Sabrina Carpenter: Short n' Sweet The Cure: Songs of a Lost World Clairo: Charm St. Vincent: All Born Screaming Doechii: Alligator Bites Never Heal Ariana Grande: eternal sunshine Kendrick Lamar: GNX Taylor Swift: THE TORTURED POETS DEPARTMENT Kali Uchis: ORQUÍDEAS |
| 13 | Bladee: Cold Visions | 2 Mount Eerie: Night Palace Clairo: Charm |
| 14 | Blood Incantation: Absolute Elsewhere | 10 Chelsea Wolfe: She Reaches Out to She Reaches Out to She Chat Pile: Cool World Judas Priest: Invincible Shield Knocked Loose: You Won't Go Before You're Supposed To Mannequin Pussy: I Got Heaven Opeth: The Last Will and Testament Gatecreeper: Dark Superstition Thou: Umbilical Vampire Weekend: Only God Was Above Us Adrianne Lenker: Bright Future |
| 15 | Bob Vylan: Humble As The Sun | Amyl and The Sniffers: Cartoon Darkness |
| 16 | Bring Me The Horizon: POST HUMAN: NeX GEn | Knocked Loose: You Won't Go Before You're Supposed To |
| 17 | Brittany Howard: What Now | 10 Common & Pete Rock: The Auditorium Vol. 1 Waxahatchee: Tigers Blood Vampire Weekend: Only God Was Above Us Adrianne Lenker: Bright Future Billie Eilish: HIT ME HARD AND SOFT The Cure: Songs of a Lost World Beyoncé: COWBOY CARTER E L U C I D: REVELATOR ScHoolboy Q: BLUE LIPS Johnny Blue Skies: Passage du Desir |
| 18 | Caribou: Honey | 3 Kim Gordon: The Collective Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks |
| 19 | Cassandra Jenkins: My Light, My Destroyer | 10 MJ Lenderman: Manning Fireworks Nala Sinephro: Endlessness Waxahatchee: Tigers Blood Nilüfer Yanya: My Method Actor Jessica Pratt: Here in the Pitch Kim Gordon: The Collective Mabe Fratti: Sentir Que No Sabes Arooj Aftab: Night Reign The Cure: Songs of a Lost World Mk.gee: Two Star & The Dream Police |
| 20 | Chanel Beads: Your Day Will Come | 5 Nala Sinephro: Endlessness Mk.gee: Two Star & The Dream Police Mannequin Pussy: I Got Heaven Cindy Lee: Diamond Jubilee MJ Lenderman: Manning Fireworks |
| 21 | Charli xcx: Brat and it's completely different but also still brat | 5 Doechii: Alligator Bites Never Heal Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT Kim Gordon: The Collective |
| 22 | Chat Pile: Cool World | 10 Blood Incantation: Absolute Elsewhere Knocked Loose: You Won't Go Before You're Supposed To Gouge Away: Deep Sage Touché Amoré: Spiral in a Straight Line Foxing: Foxing Magdalena Bay: Imaginal Disk Mannequin Pussy: I Got Heaven Cindy Lee: Diamond Jubilee Mount Eerie: Night Palace The Cure: Songs of a Lost World |
| 23 | Chelsea Wolfe: She Reaches Out to She Reaches Out to She | 10 Blood Incantation: Absolute Elsewhere Thou: Umbilical Knocked Loose: You Won't Go Before You're Supposed To Touché Amoré: Spiral in a Straight Line Geordie Greep: The New Sound Julia Holter: Something in the Room She Moves Chat Pile: Cool World Beth Gibbons: Lives Outgrown ScHoolboy Q: BLUE LIPS Mannequin Pussy: I Got Heaven |
| 24 | Chief Keef: Almighty So 2 | 10 Mk.gee: Two Star & The Dream Police Sabrina Carpenter: Short n' Sweet Nilüfer Yanya: My Method Actor Vampire Weekend: Only God Was Above Us Adrianne Lenker: Bright Future Clairo: Charm Waxahatchee: Tigers Blood Beyoncé: COWBOY CARTER Kendrick Lamar: GNX Billie Eilish: HIT ME HARD AND SOFT |
| 25 | Christopher Owens: I Wanna Run Barefoot Through Your Hair | 4 Mannequin Pussy: I Got Heaven Vampire Weekend: Only God Was Above Us MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World |
| 26 | Cindy Lee: Diamond Jubilee | 10 MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch Magdalena Bay: Imaginal Disk The Cure: Songs of a Lost World Arooj Aftab: Night Reign Waxahatchee: Tigers Blood Mdou Moctar: Funeral for Justice Mount Eerie: Night Palace Mannequin Pussy: I Got Heaven Kim Gordon: The Collective |
| 27 | claire rousay: sentiment | 10 Kim Gordon: The Collective Arooj Aftab: Night Reign MJ Lenderman: Manning Fireworks Nala Sinephro: Endlessness Mk.gee: Two Star & The Dream Police Clairo: Charm Sabrina Carpenter: Short n' Sweet Waxahatchee: Tigers Blood Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT |
| 28 | Clairo: Charm | 10 Billie Eilish: HIT ME HARD AND SOFT Mk.gee: Two Star & The Dream Police St. Vincent: All Born Screaming MJ Lenderman: Manning Fireworks Vampire Weekend: Only God Was Above Us Tyler, The Creator: CHROMAKOPIA Magdalena Bay: Imaginal Disk Adrianne Lenker: Bright Future Fontaines D.C.: Romance Sabrina Carpenter: Short n' Sweet |
| 29 | Clarissa Connelly: World of Work | 2 Kim Gordon: The Collective Nala Sinephro: Endlessness |
| 30 | Common & Pete Rock: The Auditorium Vol. 1 | 6 Vince Staples: Dark Times Brittany Howard: What Now ScHoolboy Q: BLUE LIPS The Cure: Songs of a Lost World Adrianne Lenker: Bright Future Jessica Pratt: Here in the Pitch |
| 31 | Confidence Man: 3AM (LA LA LA) | 5 The Last Dinner Party: Prelude to Ecstasy English Teacher: This Could Be Texas Billie Eilish: HIT ME HARD AND SOFT Beyoncé: COWBOY CARTER Fontaines D.C.: Romance |
| 32 | The Cure: Songs of a Lost World | 10 Fontaines D.C.: Romance MJ Lenderman: Manning Fireworks Vampire Weekend: Only God Was Above Us Waxahatchee: Tigers Blood The Last Dinner Party: Prelude to Ecstasy Jessica Pratt: Here in the Pitch Adrianne Lenker: Bright Future Jack White: No Name Nick Cave & The Bad Seeds: Wild God Mannequin Pussy: I Got Heaven |
| 33 | Denzel Curry: King of the Mischievous South Vol. 2 | 2 Beyoncé: COWBOY CARTER Tyler, The Creator: CHROMAKOPIA |
| 34 | DIIV: Frog in Boiling Water | 10 Father John Misty: Mahashmashana Vampire Weekend: Only God Was Above Us Mannequin Pussy: I Got Heaven The Cure: Songs of a Lost World Fontaines D.C.: Romance Mk.gee: Two Star & The Dream Police Adrianne Lenker: Bright Future Clairo: Charm Jessica Pratt: Here in the Pitch Sabrina Carpenter: Short n' Sweet |
| 35 | Doechii: Alligator Bites Never Heal | 10 ScHoolboy Q: BLUE LIPS Kendrick Lamar: GNX Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT Sabrina Carpenter: Short n' Sweet Kali Uchis: ORQUÍDEAS Tyler, The Creator: CHROMAKOPIA GloRilla: GLORIOUS MJ Lenderman: Manning Fireworks Mk.gee: Two Star & The Dream Police |
| 36 | Dua Lipa: Radical Optimism | 10 St. Vincent: All Born Screaming Ariana Grande: eternal sunshine Billie Eilish: HIT ME HARD AND SOFT Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER Mannequin Pussy: I Got Heaven Vampire Weekend: Only God Was Above Us Clairo: Charm The Last Dinner Party: Prelude to Ecstasy MJ Lenderman: Manning Fireworks |
| 37 | E L U C I D: REVELATOR | 10 Mdou Moctar: Funeral for Justice Nala Sinephro: Endlessness Arooj Aftab: Night Reign Brittany Howard: What Now Tyler, The Creator: CHROMAKOPIA Beth Gibbons: Lives Outgrown Blood Incantation: Absolute Elsewhere ScHoolboy Q: BLUE LIPS Jessica Pratt: Here in the Pitch Doechii: Alligator Bites Never Heal |
| 38 | Ekko Astral: pink balloons | 10 Knocked Loose: You Won't Go Before You're Supposed To Kali Uchis: ORQUÍDEAS Mk.gee: Two Star & The Dream Police ScHoolboy Q: BLUE LIPS Tyler, The Creator: CHROMAKOPIA Fontaines D.C.: Romance Doechii: Alligator Bites Never Heal Mannequin Pussy: I Got Heaven The Cure: Songs of a Lost World Jessica Pratt: Here in the Pitch |
| 39 | Empress Of: For Your Consideration | 10 Arooj Aftab: Night Reign Clairo: Charm Kali Uchis: ORQUÍDEAS Jack White: No Name Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT Kim Gordon: The Collective Waxahatchee: Tigers Blood Vampire Weekend: Only God Was Above Us Jessica Pratt: Here in the Pitch |
| 40 | English Teacher: This Could Be Texas | 10 SPRINTS: Letter To Self The Last Dinner Party: Prelude to Ecstasy Amyl and The Sniffers: Cartoon Darkness Fontaines D.C.: Romance The Cure: Songs of a Lost World Wunderhorse: Midas Magdalena Bay: Imaginal Disk Laura Marling: Patterns in Repeat Rachel Chinouriri: What A Devastating Turn of Events Waxahatchee: Tigers Blood |
| 41 | Erika de Casier: Still | 3 Nilüfer Yanya: My Method Actor Mk.gee: Two Star & The Dream Police MJ Lenderman: Manning Fireworks |
| 42 | Ezra Collective: Dance, No One's Watching | 5 Jamie xx: In Waves MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Jessica Pratt: Here in the Pitch Fontaines D.C.: Romance |
| 43 | Fabiana Palladino: Fabiana Palladino | 10 Tyla: TYLA Nala Sinephro: Endlessness MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch Kim Gordon: The Collective Nick Cave & The Bad Seeds: Wild God Jamie xx: In Waves Fontaines D.C.: Romance Mdou Moctar: Funeral for Justice Waxahatchee: Tigers Blood |
| 44 | Fat Dog: WOOF. | 2 The Last Dinner Party: Prelude to Ecstasy St. Vincent: All Born Screaming |
| 45 | Father John Misty: Mahashmashana | 10 MJ Lenderman: Manning Fireworks Vampire Weekend: Only God Was Above Us Wild Pink: Dulling The Horns The Cure: Songs of a Lost World DIIV: Frog in Boiling Water Gouge Away: Deep Sage Fontaines D.C.: Romance Mannequin Pussy: I Got Heaven Cindy Lee: Diamond Jubilee Kendrick Lamar: GNX |
| 46 | Faye Webster: Underdressed at the Symphony | 2 Clairo: Charm Billie Eilish: HIT ME HARD AND SOFT |
| 47 | Fievel Is Glauque: Rong Weicknes | Kim Gordon: The Collective |
| 48 | Floating Points: Cascade | 10 Adrianne Lenker: Bright Future Julia Holter: Something in the Room She Moves Mannequin Pussy: I Got Heaven Mount Eerie: Night Palace Jessica Pratt: Here in the Pitch Blood Incantation: Absolute Elsewhere The Last Dinner Party: Prelude to Ecstasy Arooj Aftab: Night Reign Brittany Howard: What Now Johnny Blue Skies: Passage du Desir |
| 49 | Fontaines D.C.: Romance | 10 The Cure: Songs of a Lost World The Last Dinner Party: Prelude to Ecstasy English Teacher: This Could Be Texas Mannequin Pussy: I Got Heaven Vampire Weekend: Only God Was Above Us MJ Lenderman: Manning Fireworks Tyler, The Creator: CHROMAKOPIA Adrianne Lenker: Bright Future Nick Cave & The Bad Seeds: Wild God Amyl and The Sniffers: Cartoon Darkness |
| 50 | Foxing: Foxing | 5 Chat Pile: Cool World Knocked Loose: You Won't Go Before You're Supposed To Kendrick Lamar: GNX MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World |
| 51 | Friko: Where we've been, Where we go from here | 4 Cindy Lee: Diamond Jubilee Jessica Pratt: Here in the Pitch Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks |
| 52 | Future & Metro Boomin: WE DON'T TRUST YOU | 9 Future & Metro Boomin: WE STILL DON'T TRUST YOU Vince Staples: Dark Times ScHoolboy Q: BLUE LIPS Doechii: Alligator Bites Never Heal Mk.gee: Two Star & The Dream Police Sabrina Carpenter: Short n' Sweet Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX Beyoncé: COWBOY CARTER |
| 53 | Future & Metro Boomin: WE STILL DON'T TRUST YOU | 6 Future & Metro Boomin: WE DON'T TRUST YOU Doechii: Alligator Bites Never Heal ScHoolboy Q: BLUE LIPS Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX Mk.gee: Two Star & The Dream Police |
| 54 | Gatecreeper: Dark Superstition | Blood Incantation: Absolute Elsewhere |
| 55 | Geordie Greep: The New Sound | 10 Chelsea Wolfe: She Reaches Out to She Reaches Out to She JPEGMAFIA: I LAY DOWN MY LIFE FOR YOU Julia Holter: Something in the Room She Moves Blood Incantation: Absolute Elsewhere Waxahatchee: Tigers Blood Magdalena Bay: Imaginal Disk Brittany Howard: What Now Mount Eerie: Night Palace Kim Gordon: The Collective Beth Gibbons: Lives Outgrown |
| 56 | Gillian Welch & David Rawlings: Woodland | 10 Waxahatchee: Tigers Blood Hurray for the Riff Raff: The Past Is Still Alive Beth Gibbons: Lives Outgrown The Cure: Songs of a Lost World Brittany Howard: What Now Mannequin Pussy: I Got Heaven MJ Lenderman: Manning Fireworks Nilüfer Yanya: My Method Actor Vampire Weekend: Only God Was Above Us Jessica Pratt: Here in the Pitch |
| 57 | GloRilla: GLORIOUS | 10 BigXthaPlug: TAKE CARE ScHoolboy Q: BLUE LIPS Doechii: Alligator Bites Never Heal Tyla: TYLA Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX Mk.gee: Two Star & The Dream Police Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT |
| 58 | Godspeed You! Black Emperor: “NO TITLE AS OF 13 FEBRUARY 2024 28,340 DEAD” | 10 Cindy Lee: Diamond Jubilee Magdalena Bay: Imaginal Disk Kim Gordon: The Collective Vince Staples: Dark Times English Teacher: This Could Be Texas Mannequin Pussy: I Got Heaven Adrianne Lenker: Bright Future Mount Eerie: Night Palace The Last Dinner Party: Prelude to Ecstasy Jack White: No Name |
| 59 | Gouge Away: Deep Sage | 10 High Vis: Guided Tour Chat Pile: Cool World Father John Misty: Mahashmashana Mannequin Pussy: I Got Heaven Blood Incantation: Absolute Elsewhere The Cure: Songs of a Lost World Nilüfer Yanya: My Method Actor Tyler, The Creator: CHROMAKOPIA MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch |
| 60 | Gracie Abrams: The Secret of Us | 5 Kacey Musgraves: Deeper Well Sabrina Carpenter: Short n' Sweet St. Vincent: All Born Screaming Billie Eilish: HIT ME HARD AND SOFT Beyoncé: COWBOY CARTER |
| 61 | Green Day: Saviors | The Cure: Songs of a Lost World |
| 62 | Hamish Hawk: A Firmer Hand | 2 Amyl and The Sniffers: Cartoon Darkness Jessica Pratt: Here in the Pitch |
| 63 | The Hard Quartet: The Hard Quartet | 4 MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Waxahatchee: Tigers Blood Jessica Pratt: Here in the Pitch |
| 64 | High Vis: Guided Tour | 4 Gouge Away: Deep Sage Mannequin Pussy: I Got Heaven Fontaines D.C.: Romance The Cure: Songs of a Lost World |
| 65 | Hovvdy: Hovvdy | 10 Doechii: Alligator Bites Never Heal Magdalena Bay: Imaginal Disk Father John Misty: Mahashmashana Vampire Weekend: Only God Was Above Us Nilüfer Yanya: My Method Actor MJ Lenderman: Manning Fireworks Mk.gee: Two Star & The Dream Police Mannequin Pussy: I Got Heaven Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER |
| 66 | Hurray for the Riff Raff: The Past Is Still Alive | 10 Mount Eerie: Night Palace MJ Lenderman: Manning Fireworks Beyoncé: COWBOY CARTER Gillian Welch & David Rawlings: Woodland KA: The Thief Next to Jesus Waxahatchee: Tigers Blood Mannequin Pussy: I Got Heaven Kali Uchis: ORQUÍDEAS Mdou Moctar: Funeral for Justice Jessica Pratt: Here in the Pitch |
| 67 | IDLES: TANGK | 10 Amyl and The Sniffers: Cartoon Darkness The Cure: Songs of a Lost World SPRINTS: Letter To Self The Last Dinner Party: Prelude to Ecstasy Fontaines D.C.: Romance Tyler, The Creator: CHROMAKOPIA KNEECAP: Fine Art Vampire Weekend: Only God Was Above Us Beth Gibbons: Lives Outgrown English Teacher: This Could Be Texas |
| 68 | Jack White: No Name | 10 The Cure: Songs of a Lost World Vampire Weekend: Only God Was Above Us Beyoncé: COWBOY CARTER Kendrick Lamar: GNX Tyler, The Creator: CHROMAKOPIA Waxahatchee: Tigers Blood Billie Eilish: HIT ME HARD AND SOFT Adrianne Lenker: Bright Future Jamie xx: In Waves Fontaines D.C.: Romance |
| 69 | Jamie xx: In Waves | 10 Fontaines D.C.: Romance Justice: Hyperdrama Jack White: No Name The Cure: Songs of a Lost World Tyler, The Creator: CHROMAKOPIA Nick Cave & The Bad Seeds: Wild God Jessica Pratt: Here in the Pitch Beyoncé: COWBOY CARTER Yard Act: Where's My Utopia? MJ Lenderman: Manning Fireworks |
| 70 | Jessica Pratt: Here in the Pitch | 10 MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Cindy Lee: Diamond Jubilee Waxahatchee: Tigers Blood Arooj Aftab: Night Reign Mdou Moctar: Funeral for Justice Beth Gibbons: Lives Outgrown Mannequin Pussy: I Got Heaven Kim Gordon: The Collective Doechii: Alligator Bites Never Heal |
| 71 | Jlin: Akoma | 6 Mabe Fratti: Sentir Que No Sabes Nala Sinephro: Endlessness Arooj Aftab: Night Reign Kim Gordon: The Collective Nilüfer Yanya: My Method Actor MJ Lenderman: Manning Fireworks |
| 72 | Johnny Blue Skies: Passage du Desir | 10 The Cure: Songs of a Lost World MJ Lenderman: Manning Fireworks Waxahatchee: Tigers Blood Jessica Pratt: Here in the Pitch Blood Incantation: Absolute Elsewhere Brittany Howard: What Now Adrianne Lenker: Bright Future Jack White: No Name Nilüfer Yanya: My Method Actor Mannequin Pussy: I Got Heaven |
| 73 | JPEGMAFIA: I LAY DOWN MY LIFE FOR YOU | 10 Geordie Greep: The New Sound Beth Gibbons: Lives Outgrown Kim Gordon: The Collective Blood Incantation: Absolute Elsewhere Kendrick Lamar: GNX Nala Sinephro: Endlessness Waxahatchee: Tigers Blood The Last Dinner Party: Prelude to Ecstasy Vampire Weekend: Only God Was Above Us Jessica Pratt: Here in the Pitch |
| 74 | Judas Priest: Invincible Shield | Blood Incantation: Absolute Elsewhere |
| 75 | Julia Holter: Something in the Room She Moves | 10 Adrianne Lenker: Bright Future Beth Gibbons: Lives Outgrown Arooj Aftab: Night Reign Chelsea Wolfe: She Reaches Out to She Reaches Out to She Floating Points: Cascade Geordie Greep: The New Sound Mount Eerie: Night Palace Blood Incantation: Absolute Elsewhere Kim Gordon: The Collective The Cure: Songs of a Lost World |
| 76 | Justice: Hyperdrama | 3 Jamie xx: In Waves Fontaines D.C.: Romance The Cure: Songs of a Lost World |
| 77 | KA: The Thief Next to Jesus | 10 Mach-Hommy: #RICHAXXHAITIAN Mannequin Pussy: I Got Heaven Mount Eerie: Night Palace LL COOL J: THE FORCE Cindy Lee: Diamond Jubilee Hurray for the Riff Raff: The Past Is Still Alive Kim Gordon: The Collective MJ Lenderman: Manning Fireworks Tyler, The Creator: CHROMAKOPIA Blood Incantation: Absolute Elsewhere |
| 78 | Kacey Musgraves: Deeper Well | 10 Sabrina Carpenter: Short n' Sweet Gracie Abrams: The Secret of Us Billie Eilish: HIT ME HARD AND SOFT Ariana Grande: eternal sunshine Beyoncé: COWBOY CARTER Taylor Swift: THE TORTURED POETS DEPARTMENT Kendrick Lamar: GNX Jack White: No Name Doechii: Alligator Bites Never Heal The Cure: Songs of a Lost World |
| 79 | Kali Uchis: ORQUÍDEAS | 10 Doechii: Alligator Bites Never Heal ScHoolboy Q: BLUE LIPS Billie Eilish: HIT ME HARD AND SOFT Beyoncé: COWBOY CARTER Tyla: TYLA Vampire Weekend: Only God Was Above Us Clairo: Charm Ekko Astral: pink balloons Empress Of: For Your Consideration Sabrina Carpenter: Short n' Sweet |
| 80 | Kamasi Washington: Fearless Movement | 4 Vampire Weekend: Only God Was Above Us The Cure: Songs of a Lost World Adrianne Lenker: Bright Future Billie Eilish: HIT ME HARD AND SOFT |
| 81 | KAYTRANADA: Timeless | 3 Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER The Cure: Songs of a Lost World |
| 82 | Kelly Lee Owens: Dreamstate | 10 Nia Archives: Silence Is Loud SPRINTS: Letter To Self Tyla: TYLA English Teacher: This Could Be Texas Clairo: Charm Waxahatchee: Tigers Blood Magdalena Bay: Imaginal Disk Vampire Weekend: Only God Was Above Us Sabrina Carpenter: Short n' Sweet Fontaines D.C.: Romance |
| 83 | Kendrick Lamar: GNX | 10 Doechii: Alligator Bites Never Heal Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT ScHoolboy Q: BLUE LIPS Jack White: No Name MJ Lenderman: Manning Fireworks Tyler, The Creator: CHROMAKOPIA The Cure: Songs of a Lost World Vince Staples: Dark Times Sabrina Carpenter: Short n' Sweet |
| 84 | Kim Deal: Nobody Loves You More | 10 The Cure: Songs of a Lost World Nick Cave & The Bad Seeds: Wild God English Teacher: This Could Be Texas Mannequin Pussy: I Got Heaven Billie Eilish: HIT ME HARD AND SOFT The Last Dinner Party: Prelude to Ecstasy Beyoncé: COWBOY CARTER Fontaines D.C.: Romance MJ Lenderman: Manning Fireworks Adrianne Lenker: Bright Future |
| 85 | Kim Gordon: The Collective | 10 Waxahatchee: Tigers Blood Mount Eerie: Night Palace Adrianne Lenker: Bright Future MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch Nala Sinephro: Endlessness Magdalena Bay: Imaginal Disk Cindy Lee: Diamond Jubilee Mannequin Pussy: I Got Heaven Nilüfer Yanya: My Method Actor |
| 86 | KNEECAP: Fine Art | 10 Fontaines D.C.: Romance The Last Dinner Party: Prelude to Ecstasy Adrianne Lenker: Bright Future Amyl and The Sniffers: Cartoon Darkness IDLES: TANGK English Teacher: This Could Be Texas St. Vincent: All Born Screaming Knocked Loose: You Won't Go Before You're Supposed To Tyler, The Creator: CHROMAKOPIA Clairo: Charm |
| 87 | Knocked Loose: You Won't Go Before You're Supposed To | 10 Chat Pile: Cool World Bring Me The Horizon: POST HUMAN: NeX GEn Chelsea Wolfe: She Reaches Out to She Reaches Out to She Blood Incantation: Absolute Elsewhere Lip Critic: Hex Dealer Touché Amoré: Spiral in a Straight Line Foxing: Foxing Zach Bryan: The Great American Bar Scene Magdalena Bay: Imaginal Disk The Cure: Songs of a Lost World |
| 88 | The Last Dinner Party: Prelude to Ecstasy | 10 English Teacher: This Could Be Texas St. Vincent: All Born Screaming Fontaines D.C.: Romance Adrianne Lenker: Bright Future The Cure: Songs of a Lost World Rachel Chinouriri: What A Devastating Turn of Events Vampire Weekend: Only God Was Above Us Amyl and The Sniffers: Cartoon Darkness Wunderhorse: Midas Laura Marling: Patterns in Repeat |
| 89 | Laura Marling: Patterns in Repeat | 10 The Last Dinner Party: Prelude to Ecstasy Vampire Weekend: Only God Was Above Us English Teacher: This Could Be Texas Kim Gordon: The Collective Los Campesinos!: All Hell Nadine Shah: Filthy Underneath Adrianne Lenker: Bright Future Magdalena Bay: Imaginal Disk SPRINTS: Letter To Self The Cure: Songs of a Lost World |
| 90 | The Lemon Twigs: A Dream Is All We Know | 2 Beth Gibbons: Lives Outgrown Kim Gordon: The Collective |
| 91 | Lime Garden: One More Thing | 2 English Teacher: This Could Be Texas The Last Dinner Party: Prelude to Ecstasy |
| 92 | Lip Critic: Hex Dealer | 3 Knocked Loose: You Won't Go Before You're Supposed To Mannequin Pussy: I Got Heaven Fontaines D.C.: Romance |
| 93 | Liquid Mike: Paul Bunyan's Slingshot | 2 Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks |
| 94 | LL COOL J: THE FORCE | 6 Mach-Hommy: #RICHAXXHAITIAN KA: The Thief Next to Jesus Kendrick Lamar: GNX Doechii: Alligator Bites Never Heal Mk.gee: Two Star & The Dream Police Tyler, The Creator: CHROMAKOPIA |
| 95 | Los Campesinos!: All Hell | 10 Laura Marling: Patterns in Repeat Arooj Aftab: Night Reign English Teacher: This Could Be Texas The Cure: Songs of a Lost World Nala Sinephro: Endlessness Nilüfer Yanya: My Method Actor Fontaines D.C.: Romance Magdalena Bay: Imaginal Disk Vampire Weekend: Only God Was Above Us Cindy Lee: Diamond Jubilee |
| 96 | Mabe Fratti: Sentir Que No Sabes | 10 Nala Sinephro: Endlessness Jlin: Akoma Cassandra Jenkins: My Light, My Destroyer Arooj Aftab: Night Reign MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch Kim Gordon: The Collective Nilüfer Yanya: My Method Actor Chat Pile: Cool World Cindy Lee: Diamond Jubilee |
| 97 | Mach-Hommy: #RICHAXXHAITIAN | 10 KA: The Thief Next to Jesus LL COOL J: THE FORCE Cindy Lee: Diamond Jubilee ScHoolboy Q: BLUE LIPS Doechii: Alligator Bites Never Heal Chat Pile: Cool World Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX Blood Incantation: Absolute Elsewhere Mk.gee: Two Star & The Dream Police |
| 98 | Magdalena Bay: Imaginal Disk | 10 Cindy Lee: Diamond Jubilee Mannequin Pussy: I Got Heaven Vampire Weekend: Only God Was Above Us Adrianne Lenker: Bright Future Waxahatchee: Tigers Blood MJ Lenderman: Manning Fireworks Vince Staples: Dark Times Kim Gordon: The Collective English Teacher: This Could Be Texas Chat Pile: Cool World |
| 99 | Maggie Rogers: Don't Forget Me | 10 Vince Staples: Dark Times A. G. Cook: Britpop Billie Eilish: HIT ME HARD AND SOFT Jack White: No Name ScHoolboy Q: BLUE LIPS Taylor Swift: THE TORTURED POETS DEPARTMENT Kendrick Lamar: GNX Clairo: Charm Ariana Grande: eternal sunshine Kali Uchis: ORQUÍDEAS |
| 100 | Mannequin Pussy: I Got Heaven | 10 MJ Lenderman: Manning Fireworks Waxahatchee: Tigers Blood The Cure: Songs of a Lost World Fontaines D.C.: Romance Blood Incantation: Absolute Elsewhere Magdalena Bay: Imaginal Disk Mount Eerie: Night Palace Jessica Pratt: Here in the Pitch Vampire Weekend: Only God Was Above Us Cindy Lee: Diamond Jubilee |
| 101 | The Marías: Submarine | 4 Clairo: Charm The Last Dinner Party: Prelude to Ecstasy Billie Eilish: HIT ME HARD AND SOFT Tyler, The Creator: CHROMAKOPIA |
| 102 | Mdou Moctar: Funeral for Justice | 10 MJ Lenderman: Manning Fireworks Jessica Pratt: Here in the Pitch MIKE & Tony Seltzer: Pinball Cindy Lee: Diamond Jubilee Arooj Aftab: Night Reign E L U C I D: REVELATOR Waxahatchee: Tigers Blood Jack White: No Name The Cure: Songs of a Lost World Kim Gordon: The Collective |
| 103 | Michael Kiwanuka: Small Changes | 3 Amyl and The Sniffers: Cartoon Darkness The Cure: Songs of a Lost World The Last Dinner Party: Prelude to Ecstasy |
| 104 | MIKE & Tony Seltzer: Pinball | 4 Mdou Moctar: Funeral for Justice Magdalena Bay: Imaginal Disk Cindy Lee: Diamond Jubilee The Cure: Songs of a Lost World |
| 105 | MJ Lenderman: Manning Fireworks | 10 Waxahatchee: Tigers Blood Jessica Pratt: Here in the Pitch Mannequin Pussy: I Got Heaven The Cure: Songs of a Lost World Father John Misty: Mahashmashana Cindy Lee: Diamond Jubilee Fontaines D.C.: Romance Mdou Moctar: Funeral for Justice Mk.gee: Two Star & The Dream Police Nilüfer Yanya: My Method Actor |
| 106 | Mk.gee: Two Star & The Dream Police | 10 MJ Lenderman: Manning Fireworks Clairo: Charm Tyler, The Creator: CHROMAKOPIA Doechii: Alligator Bites Never Heal Fontaines D.C.: Romance Cindy Lee: Diamond Jubilee Billie Eilish: HIT ME HARD AND SOFT Nilüfer Yanya: My Method Actor Waxahatchee: Tigers Blood Tems: Born in the Wild |
| 107 | Mount Eerie: Night Palace | 10 Kim Gordon: The Collective Hurray for the Riff Raff: The Past Is Still Alive Mannequin Pussy: I Got Heaven Bladee: Cold Visions Cindy Lee: Diamond Jubilee Chat Pile: Cool World KA: The Thief Next to Jesus Magdalena Bay: Imaginal Disk Arooj Aftab: Night Reign Floating Points: Cascade |
| 108 | Mustafa: Dunya | 10 Tyler, The Creator: CHROMAKOPIA Mk.gee: Two Star & The Dream Police ScHoolboy Q: BLUE LIPS Nilüfer Yanya: My Method Actor Kendrick Lamar: GNX Doechii: Alligator Bites Never Heal MJ Lenderman: Manning Fireworks Waxahatchee: Tigers Blood Fontaines D.C.: Romance The Cure: Songs of a Lost World |
| 109 | Nadine Shah: Filthy Underneath | 10 The Last Dinner Party: Prelude to Ecstasy Laura Marling: Patterns in Repeat Amyl and The Sniffers: Cartoon Darkness Kim Gordon: The Collective English Teacher: This Could Be Texas St. Vincent: All Born Screaming Adrianne Lenker: Bright Future Jessica Pratt: Here in the Pitch Waxahatchee: Tigers Blood Vampire Weekend: Only God Was Above Us |
| 110 | Nala Sinephro: Endlessness | 10 Mabe Fratti: Sentir Que No Sabes Kim Gordon: The Collective Arooj Aftab: Night Reign Cassandra Jenkins: My Light, My Destroyer Still House Plants: If I don't make it, I love u E L U C I D: REVELATOR Jlin: Akoma MJ Lenderman: Manning Fireworks Waxahatchee: Tigers Blood Chanel Beads: Your Day Will Come |
| 111 | Nia Archives: Silence Is Loud | 10 Kelly Lee Owens: Dreamstate Rachel Chinouriri: What A Devastating Turn of Events Tyla: TYLA The Last Dinner Party: Prelude to Ecstasy Amyl and The Sniffers: Cartoon Darkness SPRINTS: Letter To Self English Teacher: This Could Be Texas Doechii: Alligator Bites Never Heal Kali Uchis: ORQUÍDEAS Nala Sinephro: Endlessness |
| 112 | Nick Cave & The Bad Seeds: Wild God | 10 The Cure: Songs of a Lost World Fontaines D.C.: Romance St. Vincent: All Born Screaming Vampire Weekend: Only God Was Above Us Billie Eilish: HIT ME HARD AND SOFT The Last Dinner Party: Prelude to Ecstasy Adrianne Lenker: Bright Future Jamie xx: In Waves Kim Deal: Nobody Loves You More Jack White: No Name |
| 113 | Nilüfer Yanya: My Method Actor | 10 MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Waxahatchee: Tigers Blood Arooj Aftab: Night Reign Magdalena Bay: Imaginal Disk Cassandra Jenkins: My Light, My Destroyer Kim Gordon: The Collective Blood Incantation: Absolute Elsewhere Tyler, The Creator: CHROMAKOPIA Mannequin Pussy: I Got Heaven |
| 114 | Nubya Garcia: ODYSSEY | The Cure: Songs of a Lost World |
| 115 | NxWorries: WHY LAWD? | 8 Vince Staples: Dark Times ScHoolboy Q: BLUE LIPS Doechii: Alligator Bites Never Heal Tyler, The Creator: CHROMAKOPIA Vampire Weekend: Only God Was Above Us Beyoncé: COWBOY CARTER Kendrick Lamar: GNX Billie Eilish: HIT ME HARD AND SOFT |
| 116 | Opeth: The Last Will and Testament | Blood Incantation: Absolute Elsewhere |
| 117 | Pet Shop Boys: Nonetheless | 2 Waxahatchee: Tigers Blood The Cure: Songs of a Lost World |
| 118 | Rachel Chinouriri: What A Devastating Turn of Events | 10 The Last Dinner Party: Prelude to Ecstasy Nia Archives: Silence Is Loud Wunderhorse: Midas English Teacher: This Could Be Texas Billie Eilish: HIT ME HARD AND SOFT Amyl and The Sniffers: Cartoon Darkness SPRINTS: Letter To Self Fontaines D.C.: Romance The Cure: Songs of a Lost World Clairo: Charm |
| 119 | Ravyn Lenae: Bird's Eye | 2 Doechii: Alligator Bites Never Heal Mannequin Pussy: I Got Heaven |
| 120 | Rema: HEIS | 10 Tems: Born in the Wild Tyla: TYLA Mk.gee: Two Star & The Dream Police ScHoolboy Q: BLUE LIPS Sabrina Carpenter: Short n' Sweet Waxahatchee: Tigers Blood Beyoncé: COWBOY CARTER Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX MJ Lenderman: Manning Fireworks |
| 121 | Remi Wolf: Big Ideas | 10 St. Vincent: All Born Screaming A. G. Cook: Britpop Nilüfer Yanya: My Method Actor Sabrina Carpenter: Short n' Sweet Doechii: Alligator Bites Never Heal Magdalena Bay: Imaginal Disk Clairo: Charm Billie Eilish: HIT ME HARD AND SOFT Kali Uchis: ORQUÍDEAS Waxahatchee: Tigers Blood |
| 122 | Rosali: Bite Down | 4 Vampire Weekend: Only God Was Above Us Doechii: Alligator Bites Never Heal Billie Eilish: HIT ME HARD AND SOFT The Cure: Songs of a Lost World |
| 123 | Sabrina Carpenter: Short n' Sweet | 10 Billie Eilish: HIT ME HARD AND SOFT Beyoncé: COWBOY CARTER Doechii: Alligator Bites Never Heal Taylor Swift: THE TORTURED POETS DEPARTMENT Ariana Grande: eternal sunshine Kacey Musgraves: Deeper Well MJ Lenderman: Manning Fireworks Tyla: TYLA Kendrick Lamar: GNX Magdalena Bay: Imaginal Disk |
| 124 | ScHoolboy Q: BLUE LIPS | 10 Doechii: Alligator Bites Never Heal Tyler, The Creator: CHROMAKOPIA Vince Staples: Dark Times GloRilla: GLORIOUS Kendrick Lamar: GNX Kali Uchis: ORQUÍDEAS Future & Metro Boomin: WE DON'T TRUST YOU NxWorries: WHY LAWD? The Cure: Songs of a Lost World Beyoncé: COWBOY CARTER |
| 125 | Shabaka: Perceive Its Beauty, Acknowledge Its Grace | Kim Gordon: The Collective |
| 126 | The Smile: Wall of Eyes | 10 The Cure: Songs of a Lost World Fontaines D.C.: Romance The Last Dinner Party: Prelude to Ecstasy Amyl and The Sniffers: Cartoon Darkness English Teacher: This Could Be Texas Tyler, The Creator: CHROMAKOPIA Beth Gibbons: Lives Outgrown Nilüfer Yanya: My Method Actor St. Vincent: All Born Screaming Mannequin Pussy: I Got Heaven |
| 127 | SPRINTS: Letter To Self | 10 English Teacher: This Could Be Texas Amyl and The Sniffers: Cartoon Darkness IDLES: TANGK Wunderhorse: Midas The Last Dinner Party: Prelude to Ecstasy Kelly Lee Owens: Dreamstate Rachel Chinouriri: What A Devastating Turn of Events Nia Archives: Silence Is Loud Laura Marling: Patterns in Repeat Magdalena Bay: Imaginal Disk |
| 128 | St. Vincent: All Born Screaming | 10 The Last Dinner Party: Prelude to Ecstasy Billie Eilish: HIT ME HARD AND SOFT Dua Lipa: Radical Optimism The Cure: Songs of a Lost World Nick Cave & The Bad Seeds: Wild God Clairo: Charm Fontaines D.C.: Romance Vampire Weekend: Only God Was Above Us Adrianne Lenker: Bright Future Waxahatchee: Tigers Blood |
| 129 | Still House Plants: If I don't make it, I love u | 10 Nala Sinephro: Endlessness Kim Gordon: The Collective Kali Uchis: ORQUÍDEAS Arooj Aftab: Night Reign Vampire Weekend: Only God Was Above Us Laura Marling: Patterns in Repeat Nilüfer Yanya: My Method Actor Mannequin Pussy: I Got Heaven Adrianne Lenker: Bright Future Cindy Lee: Diamond Jubilee |
| 130 | Taylor Swift: THE TORTURED POETS DEPARTMENT | 10 Sabrina Carpenter: Short n' Sweet Billie Eilish: HIT ME HARD AND SOFT Beyoncé: COWBOY CARTER The Cure: Songs of a Lost World Maggie Rogers: Don't Forget Me The Last Dinner Party: Prelude to Ecstasy Kacey Musgraves: Deeper Well Vampire Weekend: Only God Was Above Us Ariana Grande: eternal sunshine Rachel Chinouriri: What A Devastating Turn of Events |
| 131 | Tems: Born in the Wild | 10 Rema: HEIS Tyla: TYLA Mk.gee: Two Star & The Dream Police Beyoncé: COWBOY CARTER Sabrina Carpenter: Short n' Sweet Clairo: Charm Billie Eilish: HIT ME HARD AND SOFT Doechii: Alligator Bites Never Heal Tyler, The Creator: CHROMAKOPIA Kendrick Lamar: GNX |
| 132 | This Is Lorelei: Box For Buddy, Box For Star | 10 Wild Pink: Dulling The Horns Being Dead: EELS Mannequin Pussy: I Got Heaven Waxahatchee: Tigers Blood Vampire Weekend: Only God Was Above Us Cindy Lee: Diamond Jubilee MJ Lenderman: Manning Fireworks Nilüfer Yanya: My Method Actor Blood Incantation: Absolute Elsewhere Magdalena Bay: Imaginal Disk |
| 133 | Thou: Umbilical | 4 Chelsea Wolfe: She Reaches Out to She Reaches Out to She Blood Incantation: Absolute Elsewhere ScHoolboy Q: BLUE LIPS The Cure: Songs of a Lost World |
| 134 | Tierra Whack: WORLD WIDE WHACK | 4 Doechii: Alligator Bites Never Heal Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER Billie Eilish: HIT ME HARD AND SOFT |
| 135 | Touché Amoré: Spiral in a Straight Line | 10 Chat Pile: Cool World Knocked Loose: You Won't Go Before You're Supposed To Chelsea Wolfe: She Reaches Out to She Reaches Out to She The Cure: Songs of a Lost World Blood Incantation: Absolute Elsewhere Mannequin Pussy: I Got Heaven Beth Gibbons: Lives Outgrown Tyler, The Creator: CHROMAKOPIA Fontaines D.C.: Romance Waxahatchee: Tigers Blood |
| 136 | Tyla: TYLA | 10 Rema: HEIS Tems: Born in the Wild Sabrina Carpenter: Short n' Sweet Kali Uchis: ORQUÍDEAS Fabiana Palladino: Fabiana Palladino Nia Archives: Silence Is Loud Doechii: Alligator Bites Never Heal GloRilla: GLORIOUS ScHoolboy Q: BLUE LIPS Kelly Lee Owens: Dreamstate |
| 137 | Tyler, The Creator: CHROMAKOPIA | 10 ScHoolboy Q: BLUE LIPS Fontaines D.C.: Romance The Cure: Songs of a Lost World Mk.gee: Two Star & The Dream Police Doechii: Alligator Bites Never Heal Waxahatchee: Tigers Blood Adrianne Lenker: Bright Future Cindy Lee: Diamond Jubilee Kendrick Lamar: GNX Jack White: No Name |
| 138 | Vampire Weekend: Only God Was Above Us | 10 Adrianne Lenker: Bright Future The Cure: Songs of a Lost World Fontaines D.C.: Romance The Last Dinner Party: Prelude to Ecstasy Jack White: No Name Magdalena Bay: Imaginal Disk Father John Misty: Mahashmashana Billie Eilish: HIT ME HARD AND SOFT MJ Lenderman: Manning Fireworks Laura Marling: Patterns in Repeat |
| 139 | Vince Staples: Dark Times | 10 ScHoolboy Q: BLUE LIPS Magdalena Bay: Imaginal Disk Common & Pete Rock: The Auditorium Vol. 1 Future & Metro Boomin: WE DON'T TRUST YOU NxWorries: WHY LAWD? Maggie Rogers: Don't Forget Me Kendrick Lamar: GNX Doechii: Alligator Bites Never Heal Jack White: No Name Beyoncé: COWBOY CARTER |
| 140 | Waxahatchee: Tigers Blood | 10 MJ Lenderman: Manning Fireworks The Cure: Songs of a Lost World Mannequin Pussy: I Got Heaven Kim Gordon: The Collective Jessica Pratt: Here in the Pitch Magdalena Bay: Imaginal Disk Adrianne Lenker: Bright Future Cindy Lee: Diamond Jubilee Fontaines D.C.: Romance Vampire Weekend: Only God Was Above Us |
| 141 | Wild Pink: Dulling The Horns | 8 This Is Lorelei: Box For Buddy, Box For Star Father John Misty: Mahashmashana Magdalena Bay: Imaginal Disk Vampire Weekend: Only God Was Above Us MJ Lenderman: Manning Fireworks Mannequin Pussy: I Got Heaven Waxahatchee: Tigers Blood Fontaines D.C.: Romance |
| 142 | Wishy: Triple Seven | 3 Mannequin Pussy: I Got Heaven Fontaines D.C.: Romance MJ Lenderman: Manning Fireworks |
| 143 | Wunderhorse: Midas | 8 The Last Dinner Party: Prelude to Ecstasy English Teacher: This Could Be Texas Rachel Chinouriri: What A Devastating Turn of Events Amyl and The Sniffers: Cartoon Darkness SPRINTS: Letter To Self Fontaines D.C.: Romance The Cure: Songs of a Lost World Billie Eilish: HIT ME HARD AND SOFT |
| 144 | Yard Act: Where's My Utopia? | 8 The Last Dinner Party: Prelude to Ecstasy Jamie xx: In Waves Beth Gibbons: Lives Outgrown Fontaines D.C.: Romance Vampire Weekend: Only God Was Above Us The Cure: Songs of a Lost World Adrianne Lenker: Bright Future MJ Lenderman: Manning Fireworks |
| 145 | Yasmin Williams: Acadia | 2 ScHoolboy Q: BLUE LIPS Vampire Weekend: Only God Was Above Us |
| 146 | Zach Bryan: The Great American Bar Scene | 6 Knocked Loose: You Won't Go Before You're Supposed To Kendrick Lamar: GNX Billie Eilish: HIT ME HARD AND SOFT The Cure: Songs of a Lost World Sabrina Carpenter: Short n' Sweet Beyoncé: COWBOY CARTER |
That's interesting to me. What's interesting to you?
[PS: Oh, here, I put this dataset up in raw interactive form, so you can play with it yourself if you want.]
¶ P.S. (and all the other letters) · 9 January 2025 listen/tech
Software ought to be playful. Life ought to be playful, and thus we should want the tools we use to live it to allow or ideally encourage us to play, to improvise and experiment and digress as much as their seriousness allows. For airplane controls or heart-surgery robots the amount of allowable playfulness is probably low, but for most of the things we deal with in software, the software's ability to do arbitrarily frivolous things with an ease proportionate to inconsequentiality is a pretty good gauge of their expressiveness.
Say, for example, you wanted to produce a list of songs you liked last year, but just one song that begins with each letter of the alphabet. "Why?", someone might ask, but I suggest that "how?" is a more interesting question.
Here's how in Curio. Read and do this stuff if you haven't already, then go to the History page, pick playlist, scroll down to the bottom and click "see the query for this". This gives you the query that produces the playlist view, which is like taking apart your toaster to find the part that goes "ding", except that you can still use the toaster normally even though you've also taken it apart.
We're going to change just two things about this query: take out the part that limits it to 100 tracks, and then group the full list of potential tracks by first letter and pick the top track for each letter-group. Here's that playlist query, in red, with the one bit we need to remove crossed out, and the line we need to add in orange.
The new line groups the songs by "letter", which it defines by taking the name of the top song and splitting it into individual characters. The split function is usually used to break up lists at commas, that sort of useful thing, but if we frivolously split on nothing, which translates as ([]) in Dactal, we get a list of individual characters. The :@1 filters this list to just the first letter in it. Then .(.of:@1) says to take each letter group and go to its first track.
Here's what this gives me from my data:
open this weird playlist in Spotify if you want; the premise is non-musical, but the music is still music...
Part of the point of making it easy to experiment is that you never really know what even the seemingly frivolous experiments are going to teach you. We might have thought we were going to get a list of 26 songs out of this, or if we had thought slightly harder we might have realized that we didn't take any steps to avoid songs that begin with punctuation marks, but there are several other maybe-intriguing things we can see here:
- uppercase and lowercase letters are different, obviously, and since song titles are not formally governed by the Chicago Manual of Style Convention, they can have any combination of cases
- accented characters like "Ç" are technically different letters, which will not be news to you if you know almost any language other than English, but maybe you don't
- there are a lot of languages in the world, and you probably do not listen to music in all of them, and neither do I, but still:
- so my obsessive fondness for Japanese kawaii metal and idol rock is most of how I got 251 letters instead of 26
But there are some other curious things here. What's going on with the first song, which seems to be called "Esquirlas", but is at the very top of the list instead of down in alphabetic order with the AaäÄBbCcÇ songs?
Let's find out. Curio is a web app, and the web is like a toolkit for building things that are easy to take apart. The adjustable screwdriver of web apps is the Inspect command. Right-click anything on a web page and pick Inspect.
Sometimes you will discover, if you do this, that the people who made the page did not expect you to, and the inside of their page is an incomprehensible mess of wires and crumbs and thus probably a fire hazard. But here's what the inside of this part of the Curio toaster looks like:
Ah! It looks like this song is called "Esquirlas" when you see it on the page, but in fact in the Spotify database it has a special character at the beginning which somehow comes out of the Spotify API as an (imaginary, I think) HTML entity. We may not think we care about this right now, but later when we're writing song-processing code and we don't understand why
What the hell is
We also learn one interesting thing about the typing I have done with my own weird fingers, because there are songs here beginning with most of the numbers. Not 6, though. There were no songs with titles beginning with the number 6 among my most-played single tracks by each artist. Is that right? We can cross-check.
Yes, apparently I did listen to "6km/h" by CHICKEN BLOW THE IDOL, and "666" by Ceres, but not as much as "rocket pencil" by CHIBLOW (as I assume we fondly refer to them) or "Humming" by Ceres. But the interesting thing I meant we see is that the numbers appear in this list in reverse order. That's my doing, because in my experience I mostly want numbers to be sorted from large to small, and in other query languages I found myself constantly having to sort by the negation of quantities to accomplish this, so I thought Dactal would be more expressive and improvisonational if the default was the other way around. But I didn't remember to handle it differently in the case where we're sorting names that begin with numbers. That's probably fixable. I'll work on fixing my toaster, that's my toaster oath.
What's yours?
[PPS from later:]
[PPPS: Hmm, also, what if we label the "of"s as we group, so that the back-references aren't so meta?]
Say, for example, you wanted to produce a list of songs you liked last year, but just one song that begins with each letter of the alphabet. "Why?", someone might ask, but I suggest that "how?" is a more interesting question.
Here's how in Curio. Read and do this stuff if you haven't already, then go to the History page, pick playlist, scroll down to the bottom and click "see the query for this". This gives you the query that produces the playlist view, which is like taking apart your toaster to find the part that goes "ding", except that you can still use the toaster normally even though you've also taken it apart.
We're going to change just two things about this query: take out the part that limits it to 100 tracks, and then group the full list of potential tracks by first letter and pick the top track for each letter-group. Here's that playlist query, in red, with the one bit we need to remove crossed out, and the line we need to add in orange.
2024 tracks full
/artist=(.track info.artists:@1),song=(.track info.name),date
|songdatepoints=(....count,sqrt)
/artist,song
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total)
#rank=songpoints,songtime
/artist
|artistpoints=(.of..songpoints..(...._,(0.5),difference)....total),
ms_played=(..of..songtime,total),
topsong=(.of:@1.of#(.of.track info.album:album_type=album),(.of.ts):@1.of.track info)
#artistpoints,ms_played:@<=100
/letter=(.topsong....name,([]),split:@1).(.of:@1)
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
/artist=(.track info.artists:@1),song=(.track info.name),date
|songdatepoints=(....count,sqrt)
/artist,song
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total)
#rank=songpoints,songtime
/artist
|artistpoints=(.of..songpoints..(...._,(0.5),difference)....total),
ms_played=(..of..songtime,total),
topsong=(.of:@1.of#(.of.track info.album:album_type=album),(.of.ts):@1.of.track info)
#artistpoints,ms_played:@<=100
/letter=(.topsong....name,([]),split:@1).(.of:@1)
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
The new line groups the songs by "letter", which it defines by taking the name of the top song and splitting it into individual characters. The split function is usually used to break up lists at commas, that sort of useful thing, but if we frivolously split on nothing, which translates as ([]) in Dactal, we get a list of individual characters. The :@1 filters this list to just the first letter in it. Then .(.of:@1) says to take each letter group and go to its first track.
Here's what this gives me from my data:
open this weird playlist in Spotify if you want; the premise is non-musical, but the music is still music...
Part of the point of making it easy to experiment is that you never really know what even the seemingly frivolous experiments are going to teach you. We might have thought we were going to get a list of 26 songs out of this, or if we had thought slightly harder we might have realized that we didn't take any steps to avoid songs that begin with punctuation marks, but there are several other maybe-intriguing things we can see here:
- uppercase and lowercase letters are different, obviously, and since song titles are not formally governed by the Chicago Manual of Style Convention, they can have any combination of cases
- accented characters like "Ç" are technically different letters, which will not be news to you if you know almost any language other than English, but maybe you don't
- there are a lot of languages in the world, and you probably do not listen to music in all of them, and neither do I, but still:
- so my obsessive fondness for Japanese kawaii metal and idol rock is most of how I got 251 letters instead of 26
But there are some other curious things here. What's going on with the first song, which seems to be called "Esquirlas", but is at the very top of the list instead of down in alphabetic order with the AaäÄBbCcÇ songs?
Let's find out. Curio is a web app, and the web is like a toolkit for building things that are easy to take apart. The adjustable screwdriver of web apps is the Inspect command. Right-click anything on a web page and pick Inspect.
Sometimes you will discover, if you do this, that the people who made the page did not expect you to, and the inside of their page is an incomprehensible mess of wires and crumbs and thus probably a fire hazard. But here's what the inside of this part of the Curio toaster looks like:
Ah! It looks like this song is called "Esquirlas" when you see it on the page, but in fact in the Spotify database it has a special character at the beginning which somehow comes out of the Spotify API as an (imaginary, I think) HTML entity. We may not think we care about this right now, but later when we're writing song-processing code and we don't understand why
songtitle == searchtitle isn't working, suddenly we might think "ohhhh, wait". And you might be tempted to think "nah, that probably won't ever happen again", but in fact it happens again just 20 lines down the page.
What the hell is
? Apparently it is a zero-width non-breaking space from the Unicode group Arabic Presentation Forms-B. What is it doing in the title of a song by the Swedish melodic power metal band Metalite? Apparently we do not know. Welcome to the wonderful world of trying to do anything with music data, or indeed pretty much any data that ever originated in humans typing with their weird fingers, which is essentially all data.
We also learn one interesting thing about the typing I have done with my own weird fingers, because there are songs here beginning with most of the numbers. Not 6, though. There were no songs with titles beginning with the number 6 among my most-played single tracks by each artist. Is that right? We can cross-check.
2024 tracks full:master_metadata_track_name~<6
Yes, apparently I did listen to "6km/h" by CHICKEN BLOW THE IDOL, and "666" by Ceres, but not as much as "rocket pencil" by CHIBLOW (as I assume we fondly refer to them) or "Humming" by Ceres. But the interesting thing I meant we see is that the numbers appear in this list in reverse order. That's my doing, because in my experience I mostly want numbers to be sorted from large to small, and in other query languages I found myself constantly having to sort by the negation of quantities to accomplish this, so I thought Dactal would be more expressive and improvisonational if the default was the other way around. But I didn't remember to handle it differently in the case where we're sorting names that begin with numbers. That's probably fixable. I'll work on fixing my toaster, that's my toaster oath.
What's yours?
[PPS from later:]
[PPPS: Hmm, also, what if we label the "of"s as we group, so that the back-references aren't so meta?]
2024 tracks full
/artist=(.track info.artists:@1),song=(.track info.name),date,of=streams
|songdatepoints=(....count,sqrt)
/artist,song,of=songdates
||songpoints=(..songdates...songdatepoints,total),
songtime=(..songdates..streams....ms_played,total)
#rank=songpoints,songtime
/artist,of=songs
|artistpoints=(..songs..songpoints..(...._,(0.5),difference)....total),
ms_played=(..songs..songtime,total),
topsong=(.songs:@1.songdates
#(.streams.track info.album:album_type=album),(.streams.ts)
:@1.streams:@1.track info)
#artistpoints,ms_played:@<=100
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
/artist=(.track info.artists:@1),song=(.track info.name),date,of=streams
|songdatepoints=(....count,sqrt)
/artist,song,of=songdates
||songpoints=(..songdates...songdatepoints,total),
songtime=(..songdates..streams....ms_played,total)
#rank=songpoints,songtime
/artist,of=songs
|artistpoints=(..songs..songpoints..(...._,(0.5),difference)....total),
ms_played=(..songs..songtime,total),
topsong=(.songs:@1.songdates
#(.streams.track info.album:album_type=album),(.streams.ts)
:@1.streams:@1.track info)
#artistpoints,ms_played:@<=100
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
¶ My year in music and yours · 6 January 2025 listen/tech
I think about music in years. 2024 was the first year in a long time that I didn't have anything to do with Spotify Wrapped, but that was never why I thought about music in years, and certainly never how. I like my music data to tell me stories about music not about graphic design, and in verifiable numbers not templated snarkiness. I want to stand at the end of one year and the brink of another and think clearly about how I've been listening, and thus living, and expansively about how I might.
You don't have to want what I want, but if you want to experiment with your Spotify listening data, I've built some tools to help you. To play, you need a computer and a Spotify premium account, and you need to do two things. One is this:
- go to your Spotify Account page from the popup menu on your profile picture
- scroll down to the evasively named "Account privacy" link and click it
- scroll down the Account privacy page to the "Download your data" section
- check the box on the right under "Extended streaming history", and then the Request Data button at the bottom
While you wait for your data, which will take a couple days, do the other thing, which is to go to Curio, my web thing for collating music-data curiosity, and follow the instructions for getting a free Spotify API key. If you've already done that, just wait impatiently.
When you get your data files from Spotify, go to the History page in Curio and follow the instructions there to load your streaming history. (If the file names in the instructions don't match the files you got, you got the wrong data so go back and request the Extended streaming history like I said.)
You can ask your own questions about your data, but to get started I've already set up a bunch of views that I wanted for myself. Here is a mercilessly geeky tour of those, in which you will learn way more than you ever wanted to know about my year in music.
overview
I listen to music a lot, but never strictly as background, so not for as much time as I could physically. Apparently I went 17 whole days last year without playing any, which I admit is alarming negligence. The count under "tracks" is the strict computer version of counting in which listening to a single and then the exact same song on the album counts as 2 different things. The number under "songs" tries to account for this, although Spotify doesn't give us public access to their internal audio fingerprints, so I just do this by counting unique artist-id/song-title pairs. The "albums" number only counts the albums where I played at least two of their actual tracks (so not separate singles), although it doesn't matter whether they were played from the album page or from playlists. The "artists" number counts only the primary artists of each track. The "genres" number counts only the genres where I listened to stuff by at least 5 different artists. The "of" link at the end tells you that I had 22,423 total streams last year, and you can click your count to see yours, because that's how it should be with data.
tracks
To me a "year in music" is the music that was released in a year. I listen mostly to new music, and all the personal 2024 stats I personally want to see are about my 2024 listening to 2024 music. But there's a "new releases"/"all releases" switcher at the top of most of these History views in Curio, and a separate "old releases" option for a few of them, in case you feel differently. The ranking of tracks is done with a points calculation that you can see for yourself by going to the "see the query for this" link below the table, but my philosophical position is that playing a song on loop for a while is better evidence of affection than playing it once, but not better evidence than returning to it deliberately over the course of different days, so I give each song the sum of 4th roots of its daily playcounts. (Why 4th roots? Square roots didn't mute the looping effect enough for my taste, and chaining two square-roots together worked enough better than I didn't bother adding a cube-root fucntion to the query-language yet. (Although there's also a way to do arbitrary math, so you could raise it to the power of a third, but let's not.)) I don't tend to repeat individual songs very much, so this view isn't my favorite way to look at my own history, but even so it's useful for cross-checking the playlist view that we'll get to at the end.
albums
I mostly listen via weekly playlists of new releases, usually with only one track per artist, and in the increasingly common case of waterfall releases where most of the tracks on an album are released individually before the whole album, it's common that I have listened to most of the contents of an album without directly playing the album much or at all. All of these, though, are cases where I cared enough to break my playlist pattern and play these whole albums. "ms_played" means milliseconds, and is the standard Spotify measurement of how much clock time you spent listening to a song independent of its duration, so if you end up doing your own queries you'll quickly get familiar with this. The query here does some fun stuff with song titles and release types to try to report the album/single dynamics of my listening, but it won't surprise me much if your listening has some edge cases I didn't catch in mine.
artists
But really, my levels of attachment to music are, and have been for years, mostly artist and genre. Humans, individually and collectively. The track and album lists are, for me, steps on the way to this artist list. At the artist level, too, I am leery of overweighting isolated looping, so my artist point system sums up the square roots of the number of unique tracks by that artist played on each listening day. The "count" column here is the total number of unique new 2024 songs I played by each artist. This list is a pretty good telling of my 2024 music story to myself. I expect the chance is low that anybody other than me knows this specific combination of specific artists well enough to make sense of it. First to Eleven is a prolific pop-punk covers band who put out new songs every week or two. TERA is a strange home-demo-sounding vocaloid project that has survived me mostly rotating out of vocaloid music because their use of artificial voices is so artificial. Phyllomedusa is frog-themed grindcore. Sifaeli Mwabuka is a Swahili gospel singer from Tanzania, and I could not have told you his name and am probably as surprised as you that I spent more than four hours listening to him, but apparently he is what happens when you try to listen to both maskandi and Sepedi wedding music despite not really knowing much about either. But mainly what this tells you, as you already knew if you read my book or ever talked to me about music for longer than five minutes, is that I love Nightwish and they had a new album this year.
popularity
My favorite one-time thing I ever did for Spotify Wrapped was the Listening Personality, in 2022. It wasn't supposed to be a one-time virality-nudge, it was supposed to be the seed of a way for humans and algorithms to talk to each other about the aspects of music-preference that are independent of what kind of music you like. Spotify, sadly, was not as interested as I was in what people actually want, nor about using algorithms to amplify curiosity instead of, say, maximizing marginal revenue, so I never got to do more with the Listening Personality codes beyond showing them to people once, but at least I obstinately insisted on Wrapped displaying the four-letter Myers-Briggs-esque codes I devised for each listener, over the graphics team's predictable attempt to reduce the 16 combinations to unexplained tarot cards. (But don't worry, they got their tarot cards the next year.)
I can't exactly reproduce the Listening Personality with just our own listening histories, because the calibration of the polarities of each axis was done holistically across the whole Spotify population. But if we lose collective breadth, we gain individual depth. Instead of a single letter-pair encoding a single score representing how popular the music you tend to listen to tends to be, here we have a heatmap in which the X-axis is your level of artist attachment (scaled to 100 for your most-played artist) and the Y-axis is those artists' global popularities (scaled to 100 for Taylor). What my heatmap tells us is that I listen very broadly to a lot of artists who are not very popular but usually are also not entirely unknown. And I also do like Taylor. If you only listen to Taylor, your heatmap will be a single red cell in the top left with a number of which I assume you will be very proud.
diversity
Two of the other three dimensions of the Listening Personality had to do with how your listening is distributed or concentrated across artists and across songs. I am a fairly extreme outlier in the direction of variety in both, which we see here as a small explosion centered in the bottom (more artists) right (more songs). If you spent the year cycling through Taylor's Versions of everything, you would end up in the top left; if just covers of "Anti-Hero", a red line down the right edge. The axes here are not normalized, because I no longer have access to everybody else's data to figure out a generalized granularity, so your own version of this view may be comically small or awkwardly large.
genres
The Wrapped story I worked on every year (although one year they substituted something else without having the guts to tell me they were going to) was the list of your Top Genres. I have taken some shit over the years for occasionally making up genre names, but if you want to know what the point of that was, there's a whole chapter about it in my book, and after seeing the 2024 Wrapped in which Spotify ditched the whole concept of genres as human musical community and generated idiotic random phrases instead, I feel 100% comfortable with what I did. Every year as we started making plans for Wrapped I said the same three things: 1) I will do the genre analysis so it's right, because these are real communities of artists and listeners in the real world and they deserve to see themselves. 2) Let's show people all their genres instead of just five. 3) And let's also let people see which artists (both in general and that you like) go with each genre, so the whole thing isn't mysterious. And every year the "creative" team said something like "Or what if we show the same unexplained list of five, but as a spinning hamburger made of radioactive sludge?"
Curio's version is obdurately sparkleless but sludgeless: all your genres, ranked with artisanally hand-tuned scoring, and clickable in your version to see exactly which of your artists make up each list.
words
My favorite story I proposed for Wrapped, knowing full-well that it would never get approved, ironically did involve generating random phrases for people, but the way I did it was from the words in the titles of the songs you played, so even this totally frivolous thing was explainable and there was a sense in which it could be correct. The key to getting a short list of pertinent words is having the whole population's listening to work with, so you can give each person the few words that most distinctively characterize their listening. Without that global data I am forced to give you lots of words instead of a few, in a weighted shuffle to avoid everybody's saying love death soundtrack day, but hopefully yours, like mine, will including something true and vulgar that will make you smile and would make lawyers panic.
durations
People talk about pop songs and attention spans both getting shorter, and we have all those milliseconds, so this one is a view of your listening (by month, for fun, across the top) distributed by how long you listened to each track (in minutes, down the left). I listen to a lot of three-minute songs. I listen to a few seconds of a lot of songs and then skip. I listen to some long songs. Apparently in October I was working on getting the track-scan timings right in Curio.
daymap
Everything you play on a streaming service is timestamped. In Spotify's case the timestamps are in UTC, so I included a timeshift option to allow you to bump them into your local time, or adjust your listening day some other way if you prefer. I'm not going to include my daymap, here, because it shows you every timestamp from the entire year and I realize looking at it that immediately reveals exactly when I traveled to a different time zone this year, and maybe that's more sharing than I need.
weekmap
Musically speaking, though, the view by hour and weekday is more relevant. Spotify spent a lot of energy, over the years, trying to believe that most people's listening is heavily determined by day and time, and if yours is, now you have a playlist feature for that in Spotify. Mine isn't. I make a playlist of new songs I want to hear every Friday, and then I cycle through it, gradually deleting songs I don't end up enjoying, for the rest of the week. Thus there is absolutely no sense in which a have different music I prefer on Tuesday afternoon vs Thursday morning. What I do have, as you see here, is a very distinct pattern of doing a lot of rapid new-song scanning while eating my lunch on Fridays. Also, hopefully I do not have any important online accounts for which the security question is "At what hour are you most likely to be asleep?"
release years
The fourth dimension in the Listening Personality was newness. I know, from data, that I am, or at least for a few years provably was, one of the handful of people in the world whose listening is most intently focused on new music. Curio thus has two different ways of looking at this particular quirk. This first one shows release years by listening months, so you can see how I listen to songs from the previous year for at least the first week of January, and how in May last year I tried and ultimately failed to find or remember one particular Ray Charles song.
song ages
My listening pattern is even clearer in this view by song age, with ascending resolution. Friday is new-release day, but I am often distracted from listening over the weekends, so Monday through Thursday I am most heavily playing the songs from the previous Friday.
poster
As you may already realize, my taste in visual design runs to the Tuftean. Don't make me justify the horizonal rule in this poster view to ET, but the order and sizes and colors here are all data-driven, and yet it's still faintly reminiscent of a concert-poster design.
10x10 grid
5x5 grid
banner
If I want a visual summary of my year in music, it should definitely be made out of the visual art that is already attached to the music, not something else.
playlist
open this playlist in Spotify
The playlist, at least for me, is the authoritative final summary. It's my year in music, so it better be made of music, and it better make sense to me when I play it.
Spotify makes you a playlist, too, called Your Top Songs. It's fine, in that so far they haven't decided to screw it up with any kind of machine learning. It's never what I want, personally, because it ignores release dates and it has mutiple songs per artist and it doesn't sort the way I want.
What I want, and thus built, is not a Top Songs playlist, but an attempt at a playlist that represents a listening year. To do this it starts from the artist list, not song list, and then tries to pick your most-played new song from each of those artists.
Here's what that looks like in Dactal query syntax, as you can see if you click the "see the query for this" link at the bottom of the playlist table, except I've added a little color-coding:
The red parts are the artist query, which goes like this:
- get all your 2024 tracks (from a previous query)
- group these by each track's first artist, song name and date
- assign the square root of the number of times you played that artist/song that day as songdatepoints
- group those artist/song/date groups by artist and song
- total the songdatepoints from all dates per artist/song
- also total the amount of time you played this artist/song
- sort and rank the artist/song groups by songpoints and then songtime (in both cases from larger to smaller)
- group those artist/song groups by artist
- assign each artist a total artistpoints value that is the sum of songpoints-.5 (to reduce the weight of lots of songs you only played once)
- total up the amount of time you listened to that artist
The playlist query inserts one more line (an annotation suboperation) to calculate each artist's topsong for the playlist by taking the artist's highest-ranking artist/song group and getting an album track if one is available from the potentially multiple tracks representing that song, and the one you streamed first otherwise.
The artist query already sorts the artists by artistpoints and then listening time, which is the same order I wanted for the playlist. The :@<=100 picks the first 100, and the final block in parentheses just constructs some nicer columns for the final view. Curio watches for query results with uri columns, and offers to make them into a playlist for you, as you'll see.
The other thing I always wanted in Wrapped and never got, however, is the ability to remove things. Sometimes it's technically true that you played a song a lot, but not emotionally true that you liked it. Or, in my case, it's technically true that the reissue of Ultravox's Lament is a 2024 release, but I don't consider new repackagings of old recordings of "Dancing With Tears in My Eyes" to be new songs, so I prefer to remove that one. The chaining nature of Dactal makes this easy. That final sorting/filtering line just becomes
with the limit filter still at the end so we get 100 tracks instead of 99 despite dropping one. It would also be possible to filter Ultravox out at the artist level, or Lament out at the album level, but it doesn't make any difference to the results.
That's all what Curio gives you, including deleting tracks:
But this still isn't quite what I want, and I am stubborn. I make playlists every week, during the year, and these sometimes represent my decisions, after a week of listening to more than one track from an album, which one to keep. And it's not always the one I played the most times. Curio already has this playlist data, or can have it, if you provide an indexing pattern at the bottom of the Playlists page. This can be as simple as * to index everything, but I have lots of playlists that I made for reasons other than my taste, so I only index these two particular sets:
(End partial matches with asterisks, and separate multiple patterns with a space, so the commas here are part of the matching patterns.)
So my own personal variation adds one more wrinkle to the artist/song-group sorting:
The songkey line computes an artist/song key-string, which the s2pa line then uses to navigate into another dataset, itself produced by a query, that indexes playlist appearances by those same artist/song keys. I could use this dataset join to sort tracks by playlist counts or dates or anything, but all I really want to do is prefer tracks that I put on a playlist to ones I didn't, if there's a choice. So the .(1) part results in a 1 for songkeys that are in the songkey to playlist apperances dataset, and nothing for songkeys that aren't, and Dactal always sorts something before nothing.
There is absolutely no way that you could possibly tell the difference between me doing this and not, nor any reason you should care, but I care. What you choose to care about, in your own data and life, is your business, but the moral function of software is to let us express our care, and thus to encourage us to realize we have it. Your data is yours. The stories it tells are your stories. You should want them to be right, and nobody but you should get to tell you what that right is.
You don't have to care, but I want you to. The more we all care about everything, the less we will tolerate it any of it being bad.
You don't have to want what I want, but if you want to experiment with your Spotify listening data, I've built some tools to help you. To play, you need a computer and a Spotify premium account, and you need to do two things. One is this:
- go to your Spotify Account page from the popup menu on your profile picture
- scroll down to the evasively named "Account privacy" link and click it
- scroll down the Account privacy page to the "Download your data" section
- check the box on the right under "Extended streaming history", and then the Request Data button at the bottom
While you wait for your data, which will take a couple days, do the other thing, which is to go to Curio, my web thing for collating music-data curiosity, and follow the instructions for getting a free Spotify API key. If you've already done that, just wait impatiently.
When you get your data files from Spotify, go to the History page in Curio and follow the instructions there to load your streaming history. (If the file names in the instructions don't match the files you got, you got the wrong data so go back and request the Extended streaming history like I said.)
You can ask your own questions about your data, but to get started I've already set up a bunch of views that I wanted for myself. Here is a mercilessly geeky tour of those, in which you will learn way more than you ever wanted to know about my year in music.
overview
I listen to music a lot, but never strictly as background, so not for as much time as I could physically. Apparently I went 17 whole days last year without playing any, which I admit is alarming negligence. The count under "tracks" is the strict computer version of counting in which listening to a single and then the exact same song on the album counts as 2 different things. The number under "songs" tries to account for this, although Spotify doesn't give us public access to their internal audio fingerprints, so I just do this by counting unique artist-id/song-title pairs. The "albums" number only counts the albums where I played at least two of their actual tracks (so not separate singles), although it doesn't matter whether they were played from the album page or from playlists. The "artists" number counts only the primary artists of each track. The "genres" number counts only the genres where I listened to stuff by at least 5 different artists. The "of" link at the end tells you that I had 22,423 total streams last year, and you can click your count to see yours, because that's how it should be with data.
tracks
To me a "year in music" is the music that was released in a year. I listen mostly to new music, and all the personal 2024 stats I personally want to see are about my 2024 listening to 2024 music. But there's a "new releases"/"all releases" switcher at the top of most of these History views in Curio, and a separate "old releases" option for a few of them, in case you feel differently. The ranking of tracks is done with a points calculation that you can see for yourself by going to the "see the query for this" link below the table, but my philosophical position is that playing a song on loop for a while is better evidence of affection than playing it once, but not better evidence than returning to it deliberately over the course of different days, so I give each song the sum of 4th roots of its daily playcounts. (Why 4th roots? Square roots didn't mute the looping effect enough for my taste, and chaining two square-roots together worked enough better than I didn't bother adding a cube-root fucntion to the query-language yet. (Although there's also a way to do arbitrary math, so you could raise it to the power of a third, but let's not.)) I don't tend to repeat individual songs very much, so this view isn't my favorite way to look at my own history, but even so it's useful for cross-checking the playlist view that we'll get to at the end.
albums
I mostly listen via weekly playlists of new releases, usually with only one track per artist, and in the increasingly common case of waterfall releases where most of the tracks on an album are released individually before the whole album, it's common that I have listened to most of the contents of an album without directly playing the album much or at all. All of these, though, are cases where I cared enough to break my playlist pattern and play these whole albums. "ms_played" means milliseconds, and is the standard Spotify measurement of how much clock time you spent listening to a song independent of its duration, so if you end up doing your own queries you'll quickly get familiar with this. The query here does some fun stuff with song titles and release types to try to report the album/single dynamics of my listening, but it won't surprise me much if your listening has some edge cases I didn't catch in mine.
artists
But really, my levels of attachment to music are, and have been for years, mostly artist and genre. Humans, individually and collectively. The track and album lists are, for me, steps on the way to this artist list. At the artist level, too, I am leery of overweighting isolated looping, so my artist point system sums up the square roots of the number of unique tracks by that artist played on each listening day. The "count" column here is the total number of unique new 2024 songs I played by each artist. This list is a pretty good telling of my 2024 music story to myself. I expect the chance is low that anybody other than me knows this specific combination of specific artists well enough to make sense of it. First to Eleven is a prolific pop-punk covers band who put out new songs every week or two. TERA is a strange home-demo-sounding vocaloid project that has survived me mostly rotating out of vocaloid music because their use of artificial voices is so artificial. Phyllomedusa is frog-themed grindcore. Sifaeli Mwabuka is a Swahili gospel singer from Tanzania, and I could not have told you his name and am probably as surprised as you that I spent more than four hours listening to him, but apparently he is what happens when you try to listen to both maskandi and Sepedi wedding music despite not really knowing much about either. But mainly what this tells you, as you already knew if you read my book or ever talked to me about music for longer than five minutes, is that I love Nightwish and they had a new album this year.
popularity
My favorite one-time thing I ever did for Spotify Wrapped was the Listening Personality, in 2022. It wasn't supposed to be a one-time virality-nudge, it was supposed to be the seed of a way for humans and algorithms to talk to each other about the aspects of music-preference that are independent of what kind of music you like. Spotify, sadly, was not as interested as I was in what people actually want, nor about using algorithms to amplify curiosity instead of, say, maximizing marginal revenue, so I never got to do more with the Listening Personality codes beyond showing them to people once, but at least I obstinately insisted on Wrapped displaying the four-letter Myers-Briggs-esque codes I devised for each listener, over the graphics team's predictable attempt to reduce the 16 combinations to unexplained tarot cards. (But don't worry, they got their tarot cards the next year.)
I can't exactly reproduce the Listening Personality with just our own listening histories, because the calibration of the polarities of each axis was done holistically across the whole Spotify population. But if we lose collective breadth, we gain individual depth. Instead of a single letter-pair encoding a single score representing how popular the music you tend to listen to tends to be, here we have a heatmap in which the X-axis is your level of artist attachment (scaled to 100 for your most-played artist) and the Y-axis is those artists' global popularities (scaled to 100 for Taylor). What my heatmap tells us is that I listen very broadly to a lot of artists who are not very popular but usually are also not entirely unknown. And I also do like Taylor. If you only listen to Taylor, your heatmap will be a single red cell in the top left with a number of which I assume you will be very proud.
diversity
Two of the other three dimensions of the Listening Personality had to do with how your listening is distributed or concentrated across artists and across songs. I am a fairly extreme outlier in the direction of variety in both, which we see here as a small explosion centered in the bottom (more artists) right (more songs). If you spent the year cycling through Taylor's Versions of everything, you would end up in the top left; if just covers of "Anti-Hero", a red line down the right edge. The axes here are not normalized, because I no longer have access to everybody else's data to figure out a generalized granularity, so your own version of this view may be comically small or awkwardly large.
genres
The Wrapped story I worked on every year (although one year they substituted something else without having the guts to tell me they were going to) was the list of your Top Genres. I have taken some shit over the years for occasionally making up genre names, but if you want to know what the point of that was, there's a whole chapter about it in my book, and after seeing the 2024 Wrapped in which Spotify ditched the whole concept of genres as human musical community and generated idiotic random phrases instead, I feel 100% comfortable with what I did. Every year as we started making plans for Wrapped I said the same three things: 1) I will do the genre analysis so it's right, because these are real communities of artists and listeners in the real world and they deserve to see themselves. 2) Let's show people all their genres instead of just five. 3) And let's also let people see which artists (both in general and that you like) go with each genre, so the whole thing isn't mysterious. And every year the "creative" team said something like "Or what if we show the same unexplained list of five, but as a spinning hamburger made of radioactive sludge?"
Curio's version is obdurately sparkleless but sludgeless: all your genres, ranked with artisanally hand-tuned scoring, and clickable in your version to see exactly which of your artists make up each list.
words
My favorite story I proposed for Wrapped, knowing full-well that it would never get approved, ironically did involve generating random phrases for people, but the way I did it was from the words in the titles of the songs you played, so even this totally frivolous thing was explainable and there was a sense in which it could be correct. The key to getting a short list of pertinent words is having the whole population's listening to work with, so you can give each person the few words that most distinctively characterize their listening. Without that global data I am forced to give you lots of words instead of a few, in a weighted shuffle to avoid everybody's saying love death soundtrack day, but hopefully yours, like mine, will including something true and vulgar that will make you smile and would make lawyers panic.
durations
People talk about pop songs and attention spans both getting shorter, and we have all those milliseconds, so this one is a view of your listening (by month, for fun, across the top) distributed by how long you listened to each track (in minutes, down the left). I listen to a lot of three-minute songs. I listen to a few seconds of a lot of songs and then skip. I listen to some long songs. Apparently in October I was working on getting the track-scan timings right in Curio.
daymap
Everything you play on a streaming service is timestamped. In Spotify's case the timestamps are in UTC, so I included a timeshift option to allow you to bump them into your local time, or adjust your listening day some other way if you prefer. I'm not going to include my daymap, here, because it shows you every timestamp from the entire year and I realize looking at it that immediately reveals exactly when I traveled to a different time zone this year, and maybe that's more sharing than I need.
weekmap
Musically speaking, though, the view by hour and weekday is more relevant. Spotify spent a lot of energy, over the years, trying to believe that most people's listening is heavily determined by day and time, and if yours is, now you have a playlist feature for that in Spotify. Mine isn't. I make a playlist of new songs I want to hear every Friday, and then I cycle through it, gradually deleting songs I don't end up enjoying, for the rest of the week. Thus there is absolutely no sense in which a have different music I prefer on Tuesday afternoon vs Thursday morning. What I do have, as you see here, is a very distinct pattern of doing a lot of rapid new-song scanning while eating my lunch on Fridays. Also, hopefully I do not have any important online accounts for which the security question is "At what hour are you most likely to be asleep?"
release years
The fourth dimension in the Listening Personality was newness. I know, from data, that I am, or at least for a few years provably was, one of the handful of people in the world whose listening is most intently focused on new music. Curio thus has two different ways of looking at this particular quirk. This first one shows release years by listening months, so you can see how I listen to songs from the previous year for at least the first week of January, and how in May last year I tried and ultimately failed to find or remember one particular Ray Charles song.
song ages
My listening pattern is even clearer in this view by song age, with ascending resolution. Friday is new-release day, but I am often distracted from listening over the weekends, so Monday through Thursday I am most heavily playing the songs from the previous Friday.
poster
As you may already realize, my taste in visual design runs to the Tuftean. Don't make me justify the horizonal rule in this poster view to ET, but the order and sizes and colors here are all data-driven, and yet it's still faintly reminiscent of a concert-poster design.
10x10 grid
5x5 grid
banner
If I want a visual summary of my year in music, it should definitely be made out of the visual art that is already attached to the music, not something else.
playlist
open this playlist in Spotify
The playlist, at least for me, is the authoritative final summary. It's my year in music, so it better be made of music, and it better make sense to me when I play it.
Spotify makes you a playlist, too, called Your Top Songs. It's fine, in that so far they haven't decided to screw it up with any kind of machine learning. It's never what I want, personally, because it ignores release dates and it has mutiple songs per artist and it doesn't sort the way I want.
What I want, and thus built, is not a Top Songs playlist, but an attempt at a playlist that represents a listening year. To do this it starts from the artist list, not song list, and then tries to pick your most-played new song from each of those artists.
Here's what that looks like in Dactal query syntax, as you can see if you click the "see the query for this" link at the bottom of the playlist table, except I've added a little color-coding:
2024 tracks full
/artist=(.track info.artists:@1),song=(.track info.name),date
|songdatepoints=(....count,sqrt)
/artist,song
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total)
#rank=songpoints,songtime
/artist
|artistpoints=(.of..songpoints..(...._,(0.5),difference)....total),
ms_played=(..of..songtime,total),
topsong=(.of:@1.of#(.of.track info.album:album_type=album),(.of.ts):@1.of.track info)
#artistpoints,ms_played:@<=100
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
/artist=(.track info.artists:@1),song=(.track info.name),date
|songdatepoints=(....count,sqrt)
/artist,song
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total)
#rank=songpoints,songtime
/artist
|artistpoints=(.of..songpoints..(...._,(0.5),difference)....total),
ms_played=(..of..songtime,total),
topsong=(.of:@1.of#(.of.track info.album:album_type=album),(.of.ts):@1.of.track info)
#artistpoints,ms_played:@<=100
.(...artistpoints=artistpoints,
duration=(.topsong....duration_ms,mmss),
artist=(.topsong.artists:@1.name),
track=(.topsong.name),
uri=(.topsong.uri))
The red parts are the artist query, which goes like this:
- get all your 2024 tracks (from a previous query)
- group these by each track's first artist, song name and date
- assign the square root of the number of times you played that artist/song that day as songdatepoints
- group those artist/song/date groups by artist and song
- total the songdatepoints from all dates per artist/song
- also total the amount of time you played this artist/song
- sort and rank the artist/song groups by songpoints and then songtime (in both cases from larger to smaller)
- group those artist/song groups by artist
- assign each artist a total artistpoints value that is the sum of songpoints-.5 (to reduce the weight of lots of songs you only played once)
- total up the amount of time you listened to that artist
The playlist query inserts one more line (an annotation suboperation) to calculate each artist's topsong for the playlist by taking the artist's highest-ranking artist/song group and getting an album track if one is available from the potentially multiple tracks representing that song, and the one you streamed first otherwise.
The artist query already sorts the artists by artistpoints and then listening time, which is the same order I wanted for the playlist. The :@<=100 picks the first 100, and the final block in parentheses just constructs some nicer columns for the final view. Curio watches for query results with uri columns, and offers to make them into a playlist for you, as you'll see.
The other thing I always wanted in Wrapped and never got, however, is the ability to remove things. Sometimes it's technically true that you played a song a lot, but not emotionally true that you liked it. Or, in my case, it's technically true that the reissue of Ultravox's Lament is a 2024 release, but I don't consider new repackagings of old recordings of "Dancing With Tears in My Eyes" to be new songs, so I prefer to remove that one. The chaining nature of Dactal makes this easy. That final sorting/filtering line just becomes
#artistpoints,ms_played:uri!=[spotify:track:3sJYcbQ3CQCpCajduB9UK2]:@<=100
with the limit filter still at the end so we get 100 tracks instead of 99 despite dropping one. It would also be possible to filter Ultravox out at the artist level, or Lament out at the album level, but it doesn't make any difference to the results.
That's all what Curio gives you, including deleting tracks:
But this still isn't quite what I want, and I am stubborn. I make playlists every week, during the year, and these sometimes represent my decisions, after a week of listening to more than one track from an album, which one to keep. And it's not always the one I played the most times. Curio already has this playlist data, or can have it, if you provide an indexing pattern at the bottom of the Playlists page. This can be as simple as * to index everything, but I have lots of playlists that I made for reasons other than my taste, so I only index these two particular sets:
(End partial matches with asterisks, and separate multiple patterns with a space, so the commas here are part of the matching patterns.)
So my own personal variation adds one more wrinkle to the artist/song-group sorting:
/artist,song
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total),
songkey=(....(.artist.id),song,concatenate),
s2pa=(.songkey.songkey to playlist appearances.(1))
#rank=s2pa,songpoints,songtime
||songpoints=(..of...songdatepoints,total),
songtime=(..of..of....ms_played,total),
songkey=(....(.artist.id),song,concatenate),
s2pa=(.songkey.songkey to playlist appearances.(1))
#rank=s2pa,songpoints,songtime
The songkey line computes an artist/song key-string, which the s2pa line then uses to navigate into another dataset, itself produced by a query, that indexes playlist appearances by those same artist/song keys. I could use this dataset join to sort tracks by playlist counts or dates or anything, but all I really want to do is prefer tracks that I put on a playlist to ones I didn't, if there's a choice. So the .(1) part results in a 1 for songkeys that are in the songkey to playlist apperances dataset, and nothing for songkeys that aren't, and Dactal always sorts something before nothing.
There is absolutely no way that you could possibly tell the difference between me doing this and not, nor any reason you should care, but I care. What you choose to care about, in your own data and life, is your business, but the moral function of software is to let us express our care, and thus to encourage us to realize we have it. Your data is yours. The stories it tells are your stories. You should want them to be right, and nobody but you should get to tell you what that right is.
You don't have to care, but I want you to. The more we all care about everything, the less we will tolerate it any of it being bad.
¶ Subgenres, subcontinents · 9 December 2024 essay/listen/tech
¶ Know Yourself (For Free) · 30 November 2024 listen/tech
It's that time of year when companies begin pretending that a) the year is already over, and b) you should be grateful to them for giving you a tiny yearly glimpse of your own data.
But your data is yours. You shouldn't have to elaborately ask for it, but that tends to be the state at the moment. You can get your streaming history from Spotify by going to Account > Security and privacy > Account privacy (of course). They're hoping you don't, because they really want you to eagerly wait for them to tell you what you listened to this year (not counting the last few weeks of it) in parsimoniously abbreviated detail and laboriously garish graphic design.
But if you do, because you can, and you get a Spotify API key, which you can, then you can play with Curio, my experimental app for organizing music curiosity. Curio does a potentially puzzling assortment of things that I like to do, but it also has a query language, so that neither of us is limited by what I already think I want.
And so, among other things, you can make your own Wrapped.
Request your data. When you get it, it'll be a zip file. Unzip it and you'll get a folder full of files. Go to the Curio query page. Hit the "Load more data" button and select all the downloaded files in that folder whose names start with "Streaming_History_Audio_". Curio will stitch them all back together for you. Now you have your listening history.
Of course, to make sense of your listening history, you would need a bit more data. But you could run this query:
listening history
:ts>=2024
:ms_played>=30000
|id=(...spotify_track_uri,([:]),split.split:@3)
|track info=(.id.other tracks)
:(.track info.album.release_date:>2024)
|date=(...ts,(T),split.split:@1)
Translated, that says:
- start with your whole listening history
- filter that to just the plays from 2024
- filter that to just the songs you played at least 30 seconds of
- perform some arcane shenanigans to extract the ID from the Spotify track URI
- use that ID to get the full track info from the Spotify API
- filter the list to just the tracks that were released in 2024, because to a music person that's what "year in music" means
- extract the date from the Spotify timestamp
Hit Enter to run that, and then type "2024 tracks full" in the query-name box to the right, and hit Enter there too.
Now you have the right data to start doing some analysis.
You could now, for example, run this query:
2024 tracks full
/artist=(.track info.artist:@1),song=(.track info.name),date
|points=(....count,sqrt....sqrt)
/artist,song
|points=(.of....points,total)
#rank=points
- take the 2024 tracks from the first query
- group them by artist, song name and date
- give each artist-song-date triplet the quad root of the number of times you played it that day (because I don't trust looping very much)
- group the artist-song-date triplets into artist-song pairs
- score the pairs by totaling their triplets
- sort and rank the pairs by points
You could save that one as "2024 songs scored". Notice that it doesn't end after 5, or even after 100.
You could also do a very similar query for artists, just skipping the song part:
2024 tracks full
/artist=(.track info.artist),date
|points=(....count,sqrt)
/artist
|points=(.of....points,total)
#rank=points
And if you save that one as "2024 artists scored", then you could also use it for:
2024 artists scored
/genre=(.artist.artist genres.genres)
|points=(....count,sqrt....total),top artist=(.of:@1.name)#rank=points
and now you have a top genre list that isn't limited to 5, shows you artist counts and your top artist for each, and because this is all data, even lets you see which artists belong to every genre (click the "of[...]" link).
But those are just questions I asked. You can ask your own.
You should always be able to ask your own questions about yourself. You should demand to be.
But your data is yours. You shouldn't have to elaborately ask for it, but that tends to be the state at the moment. You can get your streaming history from Spotify by going to Account > Security and privacy > Account privacy (of course). They're hoping you don't, because they really want you to eagerly wait for them to tell you what you listened to this year (not counting the last few weeks of it) in parsimoniously abbreviated detail and laboriously garish graphic design.
But if you do, because you can, and you get a Spotify API key, which you can, then you can play with Curio, my experimental app for organizing music curiosity. Curio does a potentially puzzling assortment of things that I like to do, but it also has a query language, so that neither of us is limited by what I already think I want.
And so, among other things, you can make your own Wrapped.
Request your data. When you get it, it'll be a zip file. Unzip it and you'll get a folder full of files. Go to the Curio query page. Hit the "Load more data" button and select all the downloaded files in that folder whose names start with "Streaming_History_Audio_". Curio will stitch them all back together for you. Now you have your listening history.
Of course, to make sense of your listening history, you would need a bit more data. But you could run this query:
listening history
:ts>=2024
:ms_played>=30000
|id=(...spotify_track_uri,([:]),split.split:@3)
|track info=(.id.other tracks)
:(.track info.album.release_date:>2024)
|date=(...ts,(T),split.split:@1)
Translated, that says:
- start with your whole listening history
- filter that to just the plays from 2024
- filter that to just the songs you played at least 30 seconds of
- perform some arcane shenanigans to extract the ID from the Spotify track URI
- use that ID to get the full track info from the Spotify API
- filter the list to just the tracks that were released in 2024, because to a music person that's what "year in music" means
- extract the date from the Spotify timestamp
Hit Enter to run that, and then type "2024 tracks full" in the query-name box to the right, and hit Enter there too.
Now you have the right data to start doing some analysis.
You could now, for example, run this query:
2024 tracks full
/artist=(.track info.artist:@1),song=(.track info.name),date
|points=(....count,sqrt....sqrt)
/artist,song
|points=(.of....points,total)
#rank=points
- take the 2024 tracks from the first query
- group them by artist, song name and date
- give each artist-song-date triplet the quad root of the number of times you played it that day (because I don't trust looping very much)
- group the artist-song-date triplets into artist-song pairs
- score the pairs by totaling their triplets
- sort and rank the pairs by points
You could save that one as "2024 songs scored". Notice that it doesn't end after 5, or even after 100.
You could also do a very similar query for artists, just skipping the song part:
2024 tracks full
/artist=(.track info.artist),date
|points=(....count,sqrt)
/artist
|points=(.of....points,total)
#rank=points
And if you save that one as "2024 artists scored", then you could also use it for:
2024 artists scored
/genre=(.artist.artist genres.genres)
|points=(....count,sqrt....total),top artist=(.of:@1.name)#rank=points
and now you have a top genre list that isn't limited to 5, shows you artist counts and your top artist for each, and because this is all data, even lets you see which artists belong to every genre (click the "of[...]" link).
But those are just questions I asked. You can ask your own.
You should always be able to ask your own questions about yourself. You should demand to be.
¶ You've had 200 years to figure out how to search for this. · 30 September 2024 listen/tech
Streaming is a great way to listen to classical music. You have to ignore essentially all algorithmic recommendation features, which are generally oriented around tracks and playlists, but this isn't a terrible general policy anyway. Find the music you want to hear, and listen to it. If there are tools that help you find stuff, that's great.
If there are tools that are supposed to help you find stuff, but don't, that's less great. I started thinking about this again after seeing a plaintive Reddit post from somebody trying, unsuccessfully, to find a classical recording composed by (Joseph) Haydn, conducted by (Eugen) Jochum and performed by (Staatskapelle) Dresden. They made it worse for themselves by trying to do it in the Spotify mobile app, but it's not a lot better in the desktop version:
The "Top result" is Haydn and Jochum, but not Dresden. The "Songs" are Haydn without Jochum or Dresden, and the Artists start with, obtusely, Jospeh Haydn's less-famous younger brother. And you see that 3 of 5 classical titles here get truncated before the most important part, despite word-wrap technology having existed for only slightly less of history than Haydn's symphonies. The page does scroll, but that's only helpful if you're looking for "Haydn Radio", which you shouldn't be, or a hot podcast about Haydn, which may or may not exist but I don't care which.
After trying a few other search variations, I was almost ready to conclude that the Haydn recordings in question were hard to find because they weren't actually on Spotify, but finally I looked them up elsewhere, and then I was able to find them on Spotify after all. Part of the problem is that album credits are usually less complete than track credits. This is an industry metadata issue, not a Spotify-specific problem, and there's no reason you should have to care about that difference any more than the Haydns do, but it means none of us are ever going to find the two albums of Haydn / Jochum / Dresden music on Spotify, because they are credited only to Haydn, whose artist page has 1465 releases.
Apple Music, of course, has a whole dedicated classical-music sub-service called Idagio, so we might expect the difference to be like night and day, and indeed it is in terms of color palettes:
Results-wise, however, a "Haydn Jochum Dresden" search here still finds only one of the four pieces it should, and a lot of irrelevance instead of the other three, and really we shouldn't need a whole bespoke streaming service just to upgrade our search success from 0/4 to 1/4.
We only need a little extra syntax to say what we want, and a tiny bit of extra post-processing to get it. Both of which I've now added to my existing research tools on everynoise.com.
The main addition is that you can append // to your track-name search (which here is blank because we're trying to find these without remembering what they're called) to filter by artist. Doing =Haydn looks for "Haydn" only in the name of the first artist, which in the case of classical releases is always the composer. +Dresden would specify matching "Dresden" in an artist name other than the first, but there's nobody named "Haydn Dresden" yet, so I didn't bother. Partial matches are fine in all cases, so we don't need to know whether Haydn is listed as Joseph or Franz Joseph, nor how to spell Eugen or Staatskapelle.
The extra bonus feature I added is the /// album bit, which says to find only tracks that appear on proper albums (as opposed to dodgy mood compilations), and then to group and sort the results by those albums. I also took the time to factor common prefixes out of the titles, which spares us only the repeated word "Symphony" in the case of the two omnibus Haydn packages above, but produces much more readable track lists in some more-common cases. Take, e.g., this:
Ah yes, Beethoven's immortal symphony "...", popularly known as "Ellipsica". All eight lines in the "Songs" section are truncated, and trust me that you don't need to know what the "artist" called "Beethoven Symphony No. 9" sounds like, unless you are currently less than six months old and suffering from colic. Why are there spaces before the commas in the "Top result" artist list, and why does every artist in the boldly labeled Artists section say "Artist" again under it?
With a little care, filtering and sorting, we can get this:
Real albums. Readable movements. No recommendations, no moods except for your own.
And none of this was even specific to classical music. You can use these same tools to find that Bowie/Jagger song about dancing:
To be fair, you could find that, plus some bonus Björk, with normal searching:
But my version has a wildcard filter so we can find all of Bowie's collaborations:
And even deduplicate them by song:
Or all the Ghetts tracks with no guests:
Or all the non-CHVRCHES songs featuring CHVRCHES:
It's not complicated. We don't need AI for this.
(Which is good, because the AI is, maybe, not quite ready...)
If there are tools that are supposed to help you find stuff, but don't, that's less great. I started thinking about this again after seeing a plaintive Reddit post from somebody trying, unsuccessfully, to find a classical recording composed by (Joseph) Haydn, conducted by (Eugen) Jochum and performed by (Staatskapelle) Dresden. They made it worse for themselves by trying to do it in the Spotify mobile app, but it's not a lot better in the desktop version:
The "Top result" is Haydn and Jochum, but not Dresden. The "Songs" are Haydn without Jochum or Dresden, and the Artists start with, obtusely, Jospeh Haydn's less-famous younger brother. And you see that 3 of 5 classical titles here get truncated before the most important part, despite word-wrap technology having existed for only slightly less of history than Haydn's symphonies. The page does scroll, but that's only helpful if you're looking for "Haydn Radio", which you shouldn't be, or a hot podcast about Haydn, which may or may not exist but I don't care which.
After trying a few other search variations, I was almost ready to conclude that the Haydn recordings in question were hard to find because they weren't actually on Spotify, but finally I looked them up elsewhere, and then I was able to find them on Spotify after all. Part of the problem is that album credits are usually less complete than track credits. This is an industry metadata issue, not a Spotify-specific problem, and there's no reason you should have to care about that difference any more than the Haydns do, but it means none of us are ever going to find the two albums of Haydn / Jochum / Dresden music on Spotify, because they are credited only to Haydn, whose artist page has 1465 releases.
Apple Music, of course, has a whole dedicated classical-music sub-service called Idagio, so we might expect the difference to be like night and day, and indeed it is in terms of color palettes:
Results-wise, however, a "Haydn Jochum Dresden" search here still finds only one of the four pieces it should, and a lot of irrelevance instead of the other three, and really we shouldn't need a whole bespoke streaming service just to upgrade our search success from 0/4 to 1/4.
We only need a little extra syntax to say what we want, and a tiny bit of extra post-processing to get it. Both of which I've now added to my existing research tools on everynoise.com.
The main addition is that you can append // to your track-name search (which here is blank because we're trying to find these without remembering what they're called) to filter by artist. Doing =Haydn looks for "Haydn" only in the name of the first artist, which in the case of classical releases is always the composer. +Dresden would specify matching "Dresden" in an artist name other than the first, but there's nobody named "Haydn Dresden" yet, so I didn't bother. Partial matches are fine in all cases, so we don't need to know whether Haydn is listed as Joseph or Franz Joseph, nor how to spell Eugen or Staatskapelle.
The extra bonus feature I added is the /// album bit, which says to find only tracks that appear on proper albums (as opposed to dodgy mood compilations), and then to group and sort the results by those albums. I also took the time to factor common prefixes out of the titles, which spares us only the repeated word "Symphony" in the case of the two omnibus Haydn packages above, but produces much more readable track lists in some more-common cases. Take, e.g., this:
Ah yes, Beethoven's immortal symphony "...", popularly known as "Ellipsica". All eight lines in the "Songs" section are truncated, and trust me that you don't need to know what the "artist" called "Beethoven Symphony No. 9" sounds like, unless you are currently less than six months old and suffering from colic. Why are there spaces before the commas in the "Top result" artist list, and why does every artist in the boldly labeled Artists section say "Artist" again under it?
With a little care, filtering and sorting, we can get this:
Real albums. Readable movements. No recommendations, no moods except for your own.
And none of this was even specific to classical music. You can use these same tools to find that Bowie/Jagger song about dancing:
To be fair, you could find that, plus some bonus Björk, with normal searching:
But my version has a wildcard filter so we can find all of Bowie's collaborations:
And even deduplicate them by song:
Or all the Ghetts tracks with no guests:
Or all the non-CHVRCHES songs featuring CHVRCHES:
It's not complicated. We don't need AI for this.
(Which is good, because the AI is, maybe, not quite ready...)
¶ New Releases by Genre: the comeback begins · 28 July 2024 listen/tech
Spotify killed the New Releases by Genre function of Every Noise at Once when they laid me off and cut off my website from its internal data sources. As I've described previously, the fact that a functional new-release tool required internal data-access, to begin with, was a result of minor structural contingencies, not conceptual or business objections, but in 10 years of working at Spotify I do not remember ever successfully persuading the API team to change a feature. If we're going to get NRbG back, we're going to have to figure out how to rebuild it with the tools we are allowed.
But since I need NRbG myself, emotionally not just logistically, I've kept experimenting with ways of recreating it. It didn't actually take me very long to build a personal version of it. Spotify still does have the best music-service API, by far, and the brute-force approach of searching for artists by genre, and then checking the catalogs of each of those artists one-by-one for new releases every week, does basically work. It just doesn't scale. I'm willing to wait a few minutes for the things I care about the most; it doesn't work to make everybody wait for everything anybody cares about. When I worked at Spotify, I could try to solve some problems for everybody at once; from outside, I am too constrained by API rate limits.
The code I wrote, however, would work for you as readily as for me. Even my "personal" tools are general-purpose, because I assume I'll be curious tomorrow about something I didn't care about today. Maybe it's more accurate to say that I tend to build tools to extend my curiosity as much as to satisfy it, or that extending and satisfying describe a propulsive cycle of curiosity more than alternative goals. I would love to inspire this same kind of curiosity in you, but I would settle for giving you some power and letting you discover what you do with it.
And a few days ago it occurred to me that I can. Or, rather, I can give you the power of my knowledge and experience embodied in code, and you can get the power of running it for yourself by signing up for your own API keys. Which is easy and free.
Here's how:
- go to developer.spotify.com
- click "Log in", and log into your regular Spotify account
- click your name in the top right, and pick Dashboard
- read and accept the developer terms of service
- on the Dashboard page, click "Create app" in the top right
-- App name: NRbG
-- App description: New Releases by Genre
-- Website: (leave blank)
-- Redirect URIs: localhost (NRbG doesn't actually use this)
-- [x] Web API (leave the others unchecked)
-- [x] I understand and agree etc.
- click Save
- on your new NRbG app page, click Settings in the top right
- click "View client secret"
- copy your "Client ID" and "Client secret"
- go to NRbG (DIY version)
- paste your client ID and secret into the boxes
- hit Enter
Now you have power.
The new version of NRbG is a little different from the old one. Instead of a list of all the genres in the world, it has a text box. Type a genre name there and hit Enter, and it will start looking for new releases by artists in or around that genre that came out in the last release week (from Saturday through Friday, because Friday is the traditional music-industry release day).
After a while it might start finding some.
The orange letters are the first letters of each song-title, and you can click on them to hear samples. If a new release has songs that already came out some other way, they will (usually) be grayed out here, like with the gray S above for the advance single "Sekuyiso Isikhathi" from THANDAZANI's album Sasibaningi.
If you click "show track URIs", at the bottom, you'll get a list of the URIs for all the new tracks from the releases you have checked in the list, which you can copy and paste into a blank (or existing) Spotify playlist (using command-C, command-V in the Spotify desktop app). There's also a "save playlist" option, which create a new playlist for you directly if you want.
Because I built this for myself, there are a few non-obvious features.
The text box actually takes a list of things, separated by + signs, and the things can each be any of these:
- a genre (e.g. maskandi or gothic symphonic metal)
- a Spotify artist link/URI
- the name of an individual artist, in quotes, like "Nightwish", although this will find the most popular with that name, so URIs are always safer
- a Spotify playlist link/URI, to be interpreted as a list of artists
- @ and then the name of a record label (e.g. @Profound Lore; the spelling has to be exactly right, but see the note later about playlists)
If your list starts with a +, the results will be added to the bottom of the current list; otherwise the current results will be replaced.
The ">>" link encodes your current parameters, so if you click that, you can then bookmark the resulting URL for reuse.
New releases for selected labels, because labels are the only thing that works properly in new-release API searching, are each shown individually, in labeled groups. Everything else in a given list is combined to make a consolidated set of artists, those artists are then checked for their closest related artists (via Fans Also Like), and the whole thus-expanded list of artists is reordered by collective relevance and then checked individually in order for new releases.
If you don't know the exact genre names you want, offhand, you can also just type a partial name and an asterisk, like metal*, and it will give you a list of all the genre names that include that word. Or you could go to everynoise.com and type an artist name into the search box in the top right to see what genres they belong to.
The words "new" and "releases", in "new releases by genre" at the top, are both actually mode controls. "new" switches back and forth between "new", for new-release mode, and "top", for one-top-release-per-artist sampler mode, not constrained by dates. "releases" cycles through "releases" (everything), "albums" (no singles or compilations) and "singles" (no albums or compilations).
If you want to get only 1 track per release, for sampler purposes, you can put "1/" before your list. Or indeed any number and then a "/". This will pick the most popular however-many tracks on each release, and gray out the rest (and exclude them from the URI list) like the non-new tracks on new releases.
You might notice that this app, although it requires your API keys, does not itself log into your Spotify account. This is intentional. Many Spotify features are personalized for you in complicated ways, if you are logged in, and for exploratory purposes we don't want that. This means, too, that this app cannot access or modify your personal information. But if you want to control its behavior by giving it more information, it can look up non-private playlists, so that's the mechanism.
If you use a playlist as input (yours or anybody else's), it will look for new releases by the primary artists of the tracks in that playlist and their related artists, but excluding the specific releases already in the playlist. So if you, like me, spend a lot of time using this tool every Friday to make a playlist of new releases you want to hear, you can put that playlist's URI back into the same tool and it will check to see if there's anything else related that you might have missed.
In addition, once you've set up your API keys and NRbG is working, the playlist-profile page in the everynoise research tools also gets a couple added features for finding new releases. Put a playlist link or URI into that view, and it already shows you genres and record labels for every track in the list. But scroll to the bottom of the page, and you'll also see something like this:
The "see new releases" line gives you three links to NRbG for different ways of expanding on this list, each with a set of parameters pre-filled from the data in this playlist.
And, for one last bonus feature, you can check an earlier week by putting that week's Friday date (in YYYY-MM-DD format) at the beginning of your input as an override, like this:
and hit Enter to get:
You can even check whole years by including just a year, although be warned, in both cases, that release-date data gets unreliable pretty quickly once you go back beyond the very recent past.
I don't know what else I'll do with this. Probably more, because it's fun. Feedback, error reports and ideas are all welcome, in the meantime.
See what you find.
But since I need NRbG myself, emotionally not just logistically, I've kept experimenting with ways of recreating it. It didn't actually take me very long to build a personal version of it. Spotify still does have the best music-service API, by far, and the brute-force approach of searching for artists by genre, and then checking the catalogs of each of those artists one-by-one for new releases every week, does basically work. It just doesn't scale. I'm willing to wait a few minutes for the things I care about the most; it doesn't work to make everybody wait for everything anybody cares about. When I worked at Spotify, I could try to solve some problems for everybody at once; from outside, I am too constrained by API rate limits.
The code I wrote, however, would work for you as readily as for me. Even my "personal" tools are general-purpose, because I assume I'll be curious tomorrow about something I didn't care about today. Maybe it's more accurate to say that I tend to build tools to extend my curiosity as much as to satisfy it, or that extending and satisfying describe a propulsive cycle of curiosity more than alternative goals. I would love to inspire this same kind of curiosity in you, but I would settle for giving you some power and letting you discover what you do with it.
And a few days ago it occurred to me that I can. Or, rather, I can give you the power of my knowledge and experience embodied in code, and you can get the power of running it for yourself by signing up for your own API keys. Which is easy and free.
Here's how:
- go to developer.spotify.com
- click "Log in", and log into your regular Spotify account
- click your name in the top right, and pick Dashboard
- read and accept the developer terms of service
- on the Dashboard page, click "Create app" in the top right
-- App name: NRbG
-- App description: New Releases by Genre
-- Website: (leave blank)
-- Redirect URIs: localhost (NRbG doesn't actually use this)
-- [x] Web API (leave the others unchecked)
-- [x] I understand and agree etc.
- click Save
- on your new NRbG app page, click Settings in the top right
- click "View client secret"
- copy your "Client ID" and "Client secret"
- go to NRbG (DIY version)
- paste your client ID and secret into the boxes
- hit Enter
Now you have power.
The new version of NRbG is a little different from the old one. Instead of a list of all the genres in the world, it has a text box. Type a genre name there and hit Enter, and it will start looking for new releases by artists in or around that genre that came out in the last release week (from Saturday through Friday, because Friday is the traditional music-industry release day).
After a while it might start finding some.
The orange letters are the first letters of each song-title, and you can click on them to hear samples. If a new release has songs that already came out some other way, they will (usually) be grayed out here, like with the gray S above for the advance single "Sekuyiso Isikhathi" from THANDAZANI's album Sasibaningi.
If you click "show track URIs", at the bottom, you'll get a list of the URIs for all the new tracks from the releases you have checked in the list, which you can copy and paste into a blank (or existing) Spotify playlist (using command-C, command-V in the Spotify desktop app). There's also a "save playlist" option, which create a new playlist for you directly if you want.
Because I built this for myself, there are a few non-obvious features.
The text box actually takes a list of things, separated by + signs, and the things can each be any of these:
- a genre (e.g. maskandi or gothic symphonic metal)
- a Spotify artist link/URI
- the name of an individual artist, in quotes, like "Nightwish", although this will find the most popular with that name, so URIs are always safer
- a Spotify playlist link/URI, to be interpreted as a list of artists
- @ and then the name of a record label (e.g. @Profound Lore; the spelling has to be exactly right, but see the note later about playlists)
If your list starts with a +, the results will be added to the bottom of the current list; otherwise the current results will be replaced.
The ">>" link encodes your current parameters, so if you click that, you can then bookmark the resulting URL for reuse.
New releases for selected labels, because labels are the only thing that works properly in new-release API searching, are each shown individually, in labeled groups. Everything else in a given list is combined to make a consolidated set of artists, those artists are then checked for their closest related artists (via Fans Also Like), and the whole thus-expanded list of artists is reordered by collective relevance and then checked individually in order for new releases.
If you don't know the exact genre names you want, offhand, you can also just type a partial name and an asterisk, like metal*, and it will give you a list of all the genre names that include that word. Or you could go to everynoise.com and type an artist name into the search box in the top right to see what genres they belong to.
The words "new" and "releases", in "new releases by genre" at the top, are both actually mode controls. "new" switches back and forth between "new", for new-release mode, and "top", for one-top-release-per-artist sampler mode, not constrained by dates. "releases" cycles through "releases" (everything), "albums" (no singles or compilations) and "singles" (no albums or compilations).
If you want to get only 1 track per release, for sampler purposes, you can put "1/" before your list. Or indeed any number and then a "/". This will pick the most popular however-many tracks on each release, and gray out the rest (and exclude them from the URI list) like the non-new tracks on new releases.
You might notice that this app, although it requires your API keys, does not itself log into your Spotify account. This is intentional. Many Spotify features are personalized for you in complicated ways, if you are logged in, and for exploratory purposes we don't want that. This means, too, that this app cannot access or modify your personal information. But if you want to control its behavior by giving it more information, it can look up non-private playlists, so that's the mechanism.
If you use a playlist as input (yours or anybody else's), it will look for new releases by the primary artists of the tracks in that playlist and their related artists, but excluding the specific releases already in the playlist. So if you, like me, spend a lot of time using this tool every Friday to make a playlist of new releases you want to hear, you can put that playlist's URI back into the same tool and it will check to see if there's anything else related that you might have missed.
In addition, once you've set up your API keys and NRbG is working, the playlist-profile page in the everynoise research tools also gets a couple added features for finding new releases. Put a playlist link or URI into that view, and it already shows you genres and record labels for every track in the list. But scroll to the bottom of the page, and you'll also see something like this:
The "see new releases" line gives you three links to NRbG for different ways of expanding on this list, each with a set of parameters pre-filled from the data in this playlist.
And, for one last bonus feature, you can check an earlier week by putting that week's Friday date (in YYYY-MM-DD format) at the beginning of your input as an override, like this:
and hit Enter to get:
You can even check whole years by including just a year, although be warned, in both cases, that release-date data gets unreliable pretty quickly once you go back beyond the very recent past.
I don't know what else I'll do with this. Probably more, because it's fun. Feedback, error reports and ideas are all welcome, in the meantime.
See what you find.
¶ Corners of the world · 25 July 2024 listen/tech
I'm keeping a running list of book-related media links at the bottom of this post, but here are a few new things from an interestingly global week:
- I'm featured in an article about AI and the future in the French magazine Usbek & Rica this month. My copy hasn't arrvied yet, and I think it's in French, so I'm as curious as anybody what I said.
- Iveta Hajdakova and Tom Hoy at the London international consulting group Stripe Partners, who I know from some work they did for Spotify while I was there, interviewed me about algorithms and music for their Viewpoints series.
- There's an interview/feature with/about me and You Have Not Yet Heard Your Favourite Song both in print and online in the Polish magazine Polityka.
- I'll be making my second ever visit to the southern hemisphere, and first to New Zealand and Australia, to appear in conversation at Going Global Music Summit 2024 in Auckland, August 29-30, and then BIGSOUND 2024 in Brisbane, September 2-6!
- I'm featured in an article about AI and the future in the French magazine Usbek & Rica this month. My copy hasn't arrvied yet, and I think it's in French, so I'm as curious as anybody what I said.
- Iveta Hajdakova and Tom Hoy at the London international consulting group Stripe Partners, who I know from some work they did for Spotify while I was there, interviewed me about algorithms and music for their Viewpoints series.
- There's an interview/feature with/about me and You Have Not Yet Heard Your Favourite Song both in print and online in the Polish magazine Polityka.
- I'll be making my second ever visit to the southern hemisphere, and first to New Zealand and Australia, to appear in conversation at Going Global Music Summit 2024 in Auckland, August 29-30, and then BIGSOUND 2024 in Brisbane, September 2-6!
¶ Filter domes, "made for you", and the kind of personalization that even makes personalization worse · 21 June 2024 listen/tech
Filter "bubbles" are charmingly weightless, delightful to pop. Sure, there's a slight soapy residue afterwards, but check your backpack: there are probably still a few old hand-sanitizer packets you shoved in there during the pandemic. Except sometimes you reach out, flirtatiously, to pop the shimmering bubble, and hit an intransigence made of polarized glass. Less bubble, more dome.
Spotify generates a lot of playlists that are "made for you", which generally means they have been aggressively adjusted to prioritize your previous listening. This is excellent for comfort, but terrible for exploration.
For example, Spotify is currently giving me a Synthwave Mix playlist on my Made For You page. I like synthwave, but I haven't been paying much focused attention to it lately, so it would be useful to me to hear what's going on there. "My" Synthwave Mix is made for me, though, so what it suggests is going on in synthwave is a) the handful of synthwave-adjacent bands I already specifically follow, and b) a lot of other bands I also already follow who are very definitely not synthwave.
I have a tool for this, though. If you stick the link to a Spotify made-for-you playlist into this:
https://everynoise.com/playlistprofile.cgi, e.g. Synthwave Mix
you can see what that playlist looks like before it gets personalized. In my case, this is almost completely different from what I end up with; only one artist* from the underlying source playlist ends up in my personalized version. That's not a lot of discovery potential. If there were a product feature to turn off the personalization, at least I could have discovered something here. Agency unlocks curiosity.
But since there are still, for the moment, better tools for genre exploration, I'm content to just ignore almost everything they make for me. In practice there is exactly one personalized Spotify playlist I use: Release Radar. This one is different because you actually do have some control over it, albeit not in a way that is totally apparent from looking at it. Release Radar will do its own inscrutable magic for you if you let it, but first it will find you new releases by artists you Follow. So if you follow enough artists, you can crowd out the "suggestions" and get a very useful release monitor. I follow 5124 artists, but you probably don't have to be that obsessive if you aren't me. Release Radar maxes out at 200 tracks. Even with 5124 artists to monitor, there are usually not more than 200 of them with new releases in any given week, so this is OK-ish. If you aren't me it's probably way more than enough.
In weeks when there are fewer than 200 new releases by artists I follow, Release Radar will fill out the rest of the 200 tracks with releases from the previous 3 weeks. This is an earnest idea, but counter-productive for me, personally, because I monitor new releases every week and I don't want to have the old tracks shown to me again as if they are new. So I generally stick my Release Radar playlist into the same playlist viewer linked above, where I can see the release dates of the tracks, and extract just the ones from the current week into a new playlist.
I usually do this first, and only really look at the new tracks once they're in the new playlist. This morning I went for a run before I'd done this, so I just put on Release Radar itself. The older tracks come at the end, and I wasn't going to be out for 12 hours, so it didn't matter. Later when I went to make my new-songs-only copy, though, I noticed that the first few tracks in my no-personalization viewer were not the same ones I had just heard. Weird. Flipping back and forth between the two views, it was clear that they were very different. Every Release Radar is unique, so my Release Radar is already filled with my artists, and thus you might think that this is the one time when "made for you" can't do any harm.
But oh, wait. Those older songs. Ugh.
Release Radar actually does assemble the list of new songs by artists I follow, like it's meant to. The pre-personalization view shows that this week 199 of my artists had new releases; only the 200th song in the underlying list is filler from a previous week. But then the made for you filter-dome snaps down, and songs I want to hear from this week are obtusely replaced with older songs by artists Spotify thinks are more familiar to me. Which are exactly the songs I am most likely to already have contemplated in the weeks when they were new. Two algorithms later, I end up with only 79 of the 199 new songs the first algorithm had in mind for me. "Catch all the latest music from artists you follow", Release Radar promises at the top. That's exactly what I want, and exactly what it could give me if it wanted to.
Algorithms, though, don't want things. We want things, and the algorithms do what they are ordered to do. I want all the latest music I might care about. Somebody who still works for Spotify wants something else. If you aren't me, maybe it still doesn't matter. If you only care about a few artists, you won't have this problem. If a streaming service only cares about people who only care about a few artists, they won't fix it**. If they don't employ enough people who care about everything, they may not even know. Maybe what they really want is to not have to care or know, and they have a comfort metric that allows them not to.
But all of this, the domes and the not caring and the not knowing, makes the world worse. I don't want to miss joy in favor of somebody else's obliviously generalized idea of my comfort. Neither should any of us.
* The one artist isn't even actually a synthwave artist. You can't really blame Spotify for that, though, as it's hardly their business to know the internal jargon of zero-cost content makers.
** One might reasonably ask why, given that I no longer work for Spotify, I haven't switched to some other streaming service, and the answer is that whatever they do or don't fix in the app, they still have the most useful programmatic API. That's how the playlist viewer works, and if you want new releases by all the artists you follow bad enough to write code, you can have that, too. And if you're me, now you do. One playlist minus one is zero. Ultimately the only person in "personalization" is the one doing it, and if you want your personalization to be personal, that person has to be you.
[23 August 2024 update: this week the post-processing reduces the underlying 200 songs in my Release Radar to just 30. That's pathetic.]
Spotify generates a lot of playlists that are "made for you", which generally means they have been aggressively adjusted to prioritize your previous listening. This is excellent for comfort, but terrible for exploration.
For example, Spotify is currently giving me a Synthwave Mix playlist on my Made For You page. I like synthwave, but I haven't been paying much focused attention to it lately, so it would be useful to me to hear what's going on there. "My" Synthwave Mix is made for me, though, so what it suggests is going on in synthwave is a) the handful of synthwave-adjacent bands I already specifically follow, and b) a lot of other bands I also already follow who are very definitely not synthwave.
I have a tool for this, though. If you stick the link to a Spotify made-for-you playlist into this:
https://everynoise.com/playlistprofile.cgi, e.g. Synthwave Mix
you can see what that playlist looks like before it gets personalized. In my case, this is almost completely different from what I end up with; only one artist* from the underlying source playlist ends up in my personalized version. That's not a lot of discovery potential. If there were a product feature to turn off the personalization, at least I could have discovered something here. Agency unlocks curiosity.
But since there are still, for the moment, better tools for genre exploration, I'm content to just ignore almost everything they make for me. In practice there is exactly one personalized Spotify playlist I use: Release Radar. This one is different because you actually do have some control over it, albeit not in a way that is totally apparent from looking at it. Release Radar will do its own inscrutable magic for you if you let it, but first it will find you new releases by artists you Follow. So if you follow enough artists, you can crowd out the "suggestions" and get a very useful release monitor. I follow 5124 artists, but you probably don't have to be that obsessive if you aren't me. Release Radar maxes out at 200 tracks. Even with 5124 artists to monitor, there are usually not more than 200 of them with new releases in any given week, so this is OK-ish. If you aren't me it's probably way more than enough.
In weeks when there are fewer than 200 new releases by artists I follow, Release Radar will fill out the rest of the 200 tracks with releases from the previous 3 weeks. This is an earnest idea, but counter-productive for me, personally, because I monitor new releases every week and I don't want to have the old tracks shown to me again as if they are new. So I generally stick my Release Radar playlist into the same playlist viewer linked above, where I can see the release dates of the tracks, and extract just the ones from the current week into a new playlist.
I usually do this first, and only really look at the new tracks once they're in the new playlist. This morning I went for a run before I'd done this, so I just put on Release Radar itself. The older tracks come at the end, and I wasn't going to be out for 12 hours, so it didn't matter. Later when I went to make my new-songs-only copy, though, I noticed that the first few tracks in my no-personalization viewer were not the same ones I had just heard. Weird. Flipping back and forth between the two views, it was clear that they were very different. Every Release Radar is unique, so my Release Radar is already filled with my artists, and thus you might think that this is the one time when "made for you" can't do any harm.
But oh, wait. Those older songs. Ugh.
Release Radar actually does assemble the list of new songs by artists I follow, like it's meant to. The pre-personalization view shows that this week 199 of my artists had new releases; only the 200th song in the underlying list is filler from a previous week. But then the made for you filter-dome snaps down, and songs I want to hear from this week are obtusely replaced with older songs by artists Spotify thinks are more familiar to me. Which are exactly the songs I am most likely to already have contemplated in the weeks when they were new. Two algorithms later, I end up with only 79 of the 199 new songs the first algorithm had in mind for me. "Catch all the latest music from artists you follow", Release Radar promises at the top. That's exactly what I want, and exactly what it could give me if it wanted to.
Algorithms, though, don't want things. We want things, and the algorithms do what they are ordered to do. I want all the latest music I might care about. Somebody who still works for Spotify wants something else. If you aren't me, maybe it still doesn't matter. If you only care about a few artists, you won't have this problem. If a streaming service only cares about people who only care about a few artists, they won't fix it**. If they don't employ enough people who care about everything, they may not even know. Maybe what they really want is to not have to care or know, and they have a comfort metric that allows them not to.
But all of this, the domes and the not caring and the not knowing, makes the world worse. I don't want to miss joy in favor of somebody else's obliviously generalized idea of my comfort. Neither should any of us.
* The one artist isn't even actually a synthwave artist. You can't really blame Spotify for that, though, as it's hardly their business to know the internal jargon of zero-cost content makers.
** One might reasonably ask why, given that I no longer work for Spotify, I haven't switched to some other streaming service, and the answer is that whatever they do or don't fix in the app, they still have the most useful programmatic API. That's how the playlist viewer works, and if you want new releases by all the artists you follow bad enough to write code, you can have that, too. And if you're me, now you do. One playlist minus one is zero. Ultimately the only person in "personalization" is the one doing it, and if you want your personalization to be personal, that person has to be you.
[23 August 2024 update: this week the post-processing reduces the underlying 200 songs in my Release Radar to just 30. That's pathetic.]
¶ You Have Not Yet Read Your Favourite Book · 20 June 2024 listen/tech
It's probably not the one I wrote. It would be weird if my book were your favorite book. It's a geeky book about music-streaming and music and algorithms and technology and curiosity and morality and where we are right now, and your favorite book should probably be an immortal novel about how we always are, or something you have re-read every year since you were 12 because it reminds you what you love and believe.
But my book about how streaming changes music is also kind of a book about loving and believing things, and the fears and joys that love and belief produce, because everything is if you really think about it, and I wrote a book about this stuff because I really think about it and didn't know how to stop.
As a method of not thinking about something any more, writing the book seems to have been fairly ineffective. I have kept thinking and writing about music and algorithms and technology and humanity. My new job, which doesn't have music anywhere in the wording of the mission, is just as fundamentally about figuring out how to use math and machines to amplify humanity instead of phase-cancelling it.
As an organized explanation of why I think streaming is good for music and music-streaming is good for humanity, though, I made it as coherent as I could. (And then a really good editor goaded me methodically into making it more coherent than that.) If you love music, you might like reading this book while you listen to whatever you are currently discovering or wondering or doubting. It's a book about discovery and wonder and productive doubt.
And it was officially published today.
You Have Not Yet Heard Your Favourite Song; Canbury Press, 2024.
US: bookshop.org or amazon.com or kindle
UK: uk.bookshop.org or amazon.co.uk or kindle UK
France (French): Hachette/Editions Marabout or Amazon.fr, September 2024
Taiwan (Chinese): ECUS Publishing House, December 2024
India (English): The Bombay Circle Press, December 2024
In London: Waterstones or Blackwells or Foyles
In Montreal: featured at Librairie Résonance
Some related links as I notice them:
- A terrific review from book_click_deo on Instagram, December 23
- An interview for Fohlapress (or here) in Brazil, December 18
- An appearance on Bob Shami's Soundbreaker podcast, published December 12
- An interview in WNYU's STATIC magazine, December 7
- The Chinese and Indian editions are both out now, December 4
- The Chinese edition is coming! November 23
- Is this the 37th best book of 2024? An audacious claim from the Telegraph, November 17
- Read all the way to the end of this roundup of the best music books of 2024 in the Telegraph for a reminder that my book is optimistic and optimism is good, November 9
- The book and I feature in this Ars Technica story about a wave of junk music with real-artist names on Spotify, October 15
- An interview in the Chartmetric blog How Music Charts, October 9
- A review in The Quietus, September 28
- Carl Wilson and I talk about my book for the Popular Music Books in Process series, September 24
- A whole bonus episode of Your Morning Coffee Podcast, August 30.
- An appearance on the NZ podcast The Fold, August 28
- A article based on an interview with Radio New Zealand in advance of appearing at Going Global, August 28
- A mention on Your Morning Coffee Podcast, August 19 (from about 7:40-10:20) teasing an upcoming special episode with me
- A (second) appearance on Your Morning Coffee Podcast, August 5 (from about 19:09-27:45)
- An interview/feature in the Polish magazine Polityka, July 27
- An interview with Tom and Iveta at Stripe Partners for their Viewpoints series, July 25
- A book citation as part of my introduction into a story about algorithms and music discovery in Mission magazine, July 17 (with the excellent pull-quote "If you don’t want algorithms to feed you passive listening, get active.")
- A conversation with Mark Richardson for the Third Bridge Creative blog, July 9
- A print rendition of the interview from the earlier German radio piece in Die Tageszeitung, July 4
- A conversation with Walt Hickey on the Numlock Sunday podcast, June 30
- An appearance on The Ray D'Arcy Show on RTE Radio 1 Ireland, June 25 (from about 26:10-50:37; clip)
- An interview in the Dutch newsletter Weekly Wav, June 25
- An appearance on Your Morning Coffee Podcast, June 24 (from about 7:00-18:00)
- A conversation on the podcast The Gist, June 20
- A short interview on Newstalk in Ireland, June 19 (with a Cactus World News shout!)
- A radio piece in German on Deutschlandfunk Kultur, June 19 (with blasts of gothic metal and wisps of theremin!)
- A "new book" mention on Tinnitist, June 16
- A review in the Telegraph, June 4; also available via Yahoo News
- A conversation with Chris Dalla Riva in his newsletter Can't Get Much Higher, May 26
- An earlier appearance on Ari Herstand's The New Music Business podcast, April 10, with some book-anticipation towards the end
- The book's page on Goodreads
But my book about how streaming changes music is also kind of a book about loving and believing things, and the fears and joys that love and belief produce, because everything is if you really think about it, and I wrote a book about this stuff because I really think about it and didn't know how to stop.
As a method of not thinking about something any more, writing the book seems to have been fairly ineffective. I have kept thinking and writing about music and algorithms and technology and humanity. My new job, which doesn't have music anywhere in the wording of the mission, is just as fundamentally about figuring out how to use math and machines to amplify humanity instead of phase-cancelling it.
As an organized explanation of why I think streaming is good for music and music-streaming is good for humanity, though, I made it as coherent as I could. (And then a really good editor goaded me methodically into making it more coherent than that.) If you love music, you might like reading this book while you listen to whatever you are currently discovering or wondering or doubting. It's a book about discovery and wonder and productive doubt.
And it was officially published today.
You Have Not Yet Heard Your Favourite Song; Canbury Press, 2024.
US: bookshop.org or amazon.com or kindle
UK: uk.bookshop.org or amazon.co.uk or kindle UK
France (French): Hachette/Editions Marabout or Amazon.fr, September 2024
Taiwan (Chinese): ECUS Publishing House, December 2024
India (English): The Bombay Circle Press, December 2024
In London: Waterstones or Blackwells or Foyles
In Montreal: featured at Librairie Résonance
Some related links as I notice them:
- A terrific review from book_click_deo on Instagram, December 23
- An interview for Fohlapress (or here) in Brazil, December 18
- An appearance on Bob Shami's Soundbreaker podcast, published December 12
- An interview in WNYU's STATIC magazine, December 7
- The Chinese and Indian editions are both out now, December 4
- The Chinese edition is coming! November 23
- Is this the 37th best book of 2024? An audacious claim from the Telegraph, November 17
- Read all the way to the end of this roundup of the best music books of 2024 in the Telegraph for a reminder that my book is optimistic and optimism is good, November 9
- The book and I feature in this Ars Technica story about a wave of junk music with real-artist names on Spotify, October 15
- An interview in the Chartmetric blog How Music Charts, October 9
- A review in The Quietus, September 28
- Carl Wilson and I talk about my book for the Popular Music Books in Process series, September 24
- A whole bonus episode of Your Morning Coffee Podcast, August 30.
- An appearance on the NZ podcast The Fold, August 28
- A article based on an interview with Radio New Zealand in advance of appearing at Going Global, August 28
- A mention on Your Morning Coffee Podcast, August 19 (from about 7:40-10:20) teasing an upcoming special episode with me
- A (second) appearance on Your Morning Coffee Podcast, August 5 (from about 19:09-27:45)
- An interview/feature in the Polish magazine Polityka, July 27
- An interview with Tom and Iveta at Stripe Partners for their Viewpoints series, July 25
- A book citation as part of my introduction into a story about algorithms and music discovery in Mission magazine, July 17 (with the excellent pull-quote "If you don’t want algorithms to feed you passive listening, get active.")
- A conversation with Mark Richardson for the Third Bridge Creative blog, July 9
- A print rendition of the interview from the earlier German radio piece in Die Tageszeitung, July 4
- A conversation with Walt Hickey on the Numlock Sunday podcast, June 30
- An appearance on The Ray D'Arcy Show on RTE Radio 1 Ireland, June 25 (from about 26:10-50:37; clip)
- An interview in the Dutch newsletter Weekly Wav, June 25
- An appearance on Your Morning Coffee Podcast, June 24 (from about 7:00-18:00)
- A conversation on the podcast The Gist, June 20
- A short interview on Newstalk in Ireland, June 19 (with a Cactus World News shout!)
- A radio piece in German on Deutschlandfunk Kultur, June 19 (with blasts of gothic metal and wisps of theremin!)
- A "new book" mention on Tinnitist, June 16
- A review in the Telegraph, June 4; also available via Yahoo News
- A conversation with Chris Dalla Riva in his newsletter Can't Get Much Higher, May 26
- An earlier appearance on Ari Herstand's The New Music Business podcast, April 10, with some book-anticipation towards the end
- The book's page on Goodreads