glenn mcdonald furia furialog · The War Against Silence · photography · songs · code · other things     ↑vF
2 May 2014 to 20 November 2013
Every Noise at Once is a readability-adjusted scatter-plot of musical genres. The music moves from high density on the left to high bounciness on the right, and from high mechanism at the top to high organism at the bottom. Although I do have more words to explain what each of these ideas means, it's maybe better to just hear how the qualities manifest themselves in actual music.  

So here are four data-generated sampler playlists of songs that demonstrate the extreme values of these two analytical dimensions:  

Density

Bounciness


Mechanism



Organism

WBUR ran a story today about The Echo Nest, including such important topics as Massive Amounts of Data and Viking Metal.  

Music Freaks At Somerville’s The Echo Nest Fuel The Engine Under Spotify’s Hood  

Meanwhile, one thing I did with our Massive Amounts of Data today is make some playlists of quiet, calm music from unlikely sources. Here, for example, are three sets from artists normally known for metal, electronica or hip hop.  


I gave a talk on music discovery and genre-mapping at the 2014 EMP Pop Conference in Seattle last weekend. My co-panelist Michaelangelo Matos had the presence of mind to record the audio from this, and mostly the talk was just audio. Actually, I was wearing some really excellent silver pants, but while talking I was behind a podium, so you aren't missing that much. The map I describe at the end is Every Noise at Once.  

So if you're curious, the talk is 16:32 long, and you can listen here:  

Every Noise at Once @ EMP Pop 2014  
 

And, although this will not make a lot of sense until after you listen to the talk, the playlist I narrate is also available here:  

In between other tasks at work today, I made some playlists.  

Music is the thing humans do best, and all the astonishing music in the world, or close enough, is now available online. This is basically more awesome than the grandest future I ever imagined as a kid.  

But that's a lot of music. How do we make any kind of sense of it, so that this vast theoretical grandness can have any kind of actual practical significance? How do you listen to anything when you can suddenly hear every noise at once?  

Those are questions I am paid to try to help answer. I've been working for a small music-intelligence startup in Somerville called The Echo Nest. We've been running the back-end data-analysis systems that supply recommendations, personalization and music-discovery ideas to a bunch of streaming music services. When I tell people this, they usually say "Like Spotify?" And I say "Yes, like Spotify."  

But although we've been working with Spotify in various capacities, and various non-Spotify developers have made applications that combine our things with Spotify's music, we haven't been running the parts of Spotify that we run for other services. This has been an ongoing personal frustration, because Spotify is the most visible on-demand streaming music service in the world, and I've been pretty convinced that we could help them do a dramatically better job.  

We are now going to get that chance. The Echo Nest has, in fact, just been wholly acquired by Spotify. Starting today, it's actually my job to try to improve essentially everything about Spotify that matters to me.  

And this is only barely the beginning. I think we are, I mean collectively as humanity, only just at the dawn of the era of infinite music. The current streaming-music interaction-models and feature-sets are as much vestiges of our past technical constraints as anything else. It's as if we have jumped from the horse-drawn carriage to the free personal teleporter, suddenly, without the intervening benefit of even basic maps, never mind language translators or cultural history or GPS.  

For the world of music to become something we actually inhabit, natively, as opposed to a bunch of awkward phone icons into which we try to contort our curiosity and wonder, or a vast unknown from which we cower and seek familiar comfortable retreats, it's going to take a lot more than "Play me more stuff like Dave Matthews, but do a better job of it." It's going to require that we belatedly render this vast world navigable, and chart it accurately and compellingly, and put sensible enough control panels on the teleporters that you have some prayer of not just constantly zapping yourself 60' deep into an exotic undiscovered faraway cliff face.  

So that's what I'm going to be working on now.  
 

[PS: I no longer remember anything memorable or inspiring or even intelligible anybody ever said to introduce my previous acquisitions, but by way of explaining the Echo Nest purchase, Spotify CEO Daniel Ek said this: "At Spotify, we want to get people to listen to more music."]  

[PPS: And it's going to take a little while to get Echo Nest + Spotify things actually hooked up and working, but here's some music to listen to in the meantime.]  

My colleague Paul Lamere, at The Echo Nest, has been looking at music listening by listener region and artist: most distinctive artists, favorite artists and anti-preferences.  

Here, to go along with that, is a breakdown of music genres by the home cities of the artists responsible:  

We Built This City On  

Some of these are kind of duh, like "chicago house" being the top genre for Chicago. But as always in data-anything, some obviousness is what gives us the courage to believe the things we didn't already think.
On a plane flight to Dallas for the funeral of one of my dearest and nearest-to-life-long friends, I distracted myself from sadness and terror by prodding some music out of my iPad. On the way back home I wrote some words to go with it, fragments of a song of remembrance, or at least of imagination in absentia. Later, after Bethany pointed out that for once I'd written too few words, I wrote some more. Today I sang them, over and over again.  

Those of you who have not before listened to any of my own music should be warned that it is cheerfully devoid of technical virtues, but for the moment I have chosen to treat this as a style.  

Those of you who did not know Tex are really not at much of a disadvantage for understanding what is going on in the song, but you missed one of the most unforgettable and unmistakable people I ever met, and one who carried me, at times literally, through much of my childhood. He deserves far better than this maudlin, lurching song, but once you're dead you can't do anything about the ways people make up to miss you.  

The Heart of the Sky (3:23)
A couple recent things elsewhere:  

- I was interviewed for a new Brazilian arts site called Galileu, and you can read the published translation to Portuguese, or the amusingly incoherent reverse-translation back to English by Google Translate.
- I wrote a short introduction to Every Noise at Once for the stats-in-use site Significance.
- Every Noise at Once itself now has a couple bonus maps for the hottest songs of 2013 and the most retrospectively prescient 2013 finds from The Echo Nest Discovery list.
Paul Lamere wrote a post on Music Machinery yesterday about deep artists, where by "deep" we mean the opposite of one-hit wonders, artists with large, rich catalogs awaiting your listening and exploration. Paul and I both work at The Echo Nest, trying in one way or another to make sense out of the vast amount of music data the company collects.  

Often there is more than one sense to be made of the same data. Sometimes many more than one. I liked Paul's intro about one-hit hits as "non-nutritious" music, with "deep" artists as the converse nutritious music, but after that statement of concept, it seemed painfully ironic to me that his calculations resulted in the #1 score for depth going to the Vitamin String Quartet, which I think of as the artificially-fortified sugar-coated cereal of music. Their catalog, like that of the Glee Cast at #3, is certainly vast and consistent. But I wanted to measure a different kind of depth.  

So I ran some different calculations, and made a different list. For this one I took 10k or so reasonably well-known artists, and for each artist calculated an inversely-weighted average popularity of their 100 most popular songs excluding their top 10. If Paul's list is the opposite of one-hit-ness, then mine is the opposite of ten-hit-ness. I'm trying to find the artists whose catalogs are not necessarily the most vast, but where the vastness has been explored most rewardingly, artists where their 100th (or 110th) hit is still empirically world-class.  

The good news is that this worked. The bad news is that the resulting list is more than a little boring. We might not have been able to guess this ordering, exactly, but very few of these artists below stand out as fundamentally surprising. Yes, yawn, the Beatles at #1. Eminem at #2 hints at a novelty that the rest of the list doesn't really continue to deliver. I hadn't heard of Argentine singer Andrés Calamaro at #33, but he has won a Latin Grammy and sold millions of records, so that's my ignorance. Conversely, I love Nightwish and Ludovico Einaudi, but didn't fully realize how many other people do, too. Otherwise, yeah, you probably knew about these people already.  

But sometimes data reveals new truths, and sometimes, like this, it confirms existing ones. And revealing new truths is cooler, but only if the new truths actually have truth to them, and the best way to confirm our ability to generate true truths is to sometimes generate predictable true truths predictably. Anybody who presumes to do data-driven music discovery ought to have to show that they know what the opposite of "discovery" is. If purported math for "nutritious" doesn't mostly start with vegetables you already know you're supposed to be eating (like Paul's has Bach, Vivaldi and Chopin at #2, 4 and 5), don't trust it.  

So the occasional boring is OK, even good, and in that good boring spirit, here's my good boring version of the deepest artists:

  1. The Beatles
  2. Eminem
  3. Pink Floyd
  4. Iron Maiden
  5. Red Hot Chili Peppers
  6. Radiohead
  7. David Bowie
  8. Metallica
  9. Depeche Mode
  10. Bob Dylan
  11. Muse
  12. Coldplay
  13. Green Day
  14. Korn
  15. Tom Waits
  16. Rihanna
  17. Kanye West
  18. Megadeth
  19. Daft Punk
  20. The Smashing Pumpkins
  21. Nine Inch Nails
  22. The Cure
  23. Madonna
  24. Linkin Park
  25. AC/DC
  26. Beyoncé
  27. The Rolling Stones
  28. Queen
  29. Britney Spears
  30. The National
  31. Jay-Z
  32. Bruce Springsteen
  33. Andrés Calamaro
  34. Johnny Cash
  35. John Mayer
  36. U2
  37. blink-182
  38. Placebo
  39. Christina Aguilera
  40. The Offspring
  41. The Black Keys
  42. Foo Fighters
  43. Taylor Swift
  44. Michael Jackson
  45. Lady Gaga
  46. Moby
  47. Marilyn Manson
  48. Nightwish
  49. Mariah Carey
  50. Jack Johnson
  51. Black Sabbath
  52. Nirvana
  53. Motörhead
  54. Nick Cave & The Bad Seeds
  55. Avril Lavigne
  56. Arctic Monkeys
  57. Bob Marley
  58. Judas Priest
  59. Oasis
  60. Blur
  61. Queens of the Stone Age
  62. Beastie Boys
  63. Death Cab for Cutie
  64. Céline Dion
  65. Gorillaz
  66. Slayer
  67. R.E.M.
  68. Jimi Hendrix
  69. Rise Against
  70. Parov Stelar
  71. Dream Theater
  72. The White Stripes
  73. The Doors
  74. Bad Religion
  75. In Flames
  76. Maroon 5
  77. Beck
  78. The Killers
  79. Michael Bublé
  80. Modest Mouse
  81. Hans Zimmer
  82. 2Pac
  83. Massive Attack
  84. Elliott Smith
  85. Bon Jovi
  86. Robbie Williams
  87. The Ramones
  88. Ludovico Einaudi
  89. Aerosmith
  90. The Who
  91. Sting
  92. Scorpions
  93. Sufjan Stevens
  94. Lou Reed
  95. Ayreon
  96. Rush
  97. NOFX
  98. Paul McCartney
  99. Neil Young
  100. Nas
I kind of overprepared for the recent Boston Music Hackday. I ended up presenting a cheerfully simplistic but surprisingly effective automatic chorus-finder, which might conceivably have some future function at work, but that was actually my 5th hack idea, after I accidentally finished the first 4 before hacking was supposed to officially start.  

#1, which I accidentally did weeks in advance, was Is This Band Name Taken?, which is pretty literally the simplest possible Echo Nest API application, as it calls a single API function and doesn't even check the result other than to see if there is one, but still got me and the Echo Nest into the Boston Herald yesterday.  

#s 2, 3, 4 and 6 (even #5 turned out to require less work than I pessimistically anticipated) all had various things to do with the Rdio API, and eventually I realized that I was actually writing an alternate Rdio client one disconnected feature at a time, so I put them together. And gave the assemblage a name. And you can try it.  

It is called Supercollector. I think this is a pretty good name for a music-management application, and I apologize for squandering it on one that I do not expect to have particularly widespread appeal. Rdio's own software is lovely, and a big part of why Rdio is clearly the best streaming music service anybody has so far made, and my construction of an alternative is intended as a minor addition, not any kind of replacement.  

That said, there are a variety of fiddly workflow-ish things I do in the course of exploring music, and Supercollector is designed to do those in a ruthlessly streamlined way. It facilitates using one playlist as a candidate list for another. It turns search results into playlists. It turns artist catalogs into playlists. It lets you poke obsessively through playlists with an obsessive minimum of extra pointer movements. It lets you move whole albums into and out of playlists. It replaces playlist tracks that have annoyingly become unavailable since you picked them. It keeps track of your places in multiple playlists at once.  

These are obscure needs, but if they also happen to be among your obscure needs, give it a try.  

 

[Obviously you need an Rdio account. I mean, in general. To use Supercollector you probably also need to have some Rdio playlists, otherwise you're going to get a big empty screen with "Supercollector" at the top.]
Site contents published by glenn mcdonald under a Creative Commons BY/NC/ND License except where otherwise noted.