22 March 2013 to 25 September 2012
¶ A Better Picture of Every Noise at Once · 22 March 2013
I've been periodically experimenting with other tools for generating bubble-chart pictures of the music-genre space, and after tweaking and cajoling and giving up on several tools it finally occurred to me that maybe the thing I didn't like about my bubble-charts was the bubbles.
And the other obvious wrong thing was that it was just a picture. So here's a new version that is more legible (albeit not necessarily more intelligible), and in which you can hear examples of each genre by clicking on them.
Yes, that's better.
[The audio requires a modern browser like Safari or Chrome...]
[Updated 3/29 with yet another rearrangement.]
[Updated 4/1 with a "scan" feature. Like the whole planet is your car-radio.]
And the other obvious wrong thing was that it was just a picture. So here's a new version that is more legible (albeit not necessarily more intelligible), and in which you can hear examples of each genre by clicking on them.
Yes, that's better.
[The audio requires a modern browser like Safari or Chrome...]
[Updated 3/29 with yet another rearrangement.]
[Updated 4/1 with a "scan" feature. Like the whole planet is your car-radio.]
¶ Pictures of Every Noise at Once · 27 February 2013
At work today I found myself making pictures. I should probably get a better tool for making these, so they could be huge and interactive and more intelligible, but there's also something very fitting about this chaotic, overlapping state. Click to see them just barely big enough to read. The third one is actually the biggest and most readable at full size.
[Update: several people have asked me, very reasonably, what the axes and dimensions are in these. Although there are answers, technically, I think that's not really the point here. These pictures are interesting to me precisely without legends and units and grid lines. They are not explanations, they are questions. Are there elements of your experience of music that map to what you see here? Do the things you like, or don't like, cluster or align? Is this a picture of our world, or something else?]
[Update: several people have asked me, very reasonably, what the axes and dimensions are in these. Although there are answers, technically, I think that's not really the point here. These pictures are interesting to me precisely without legends and units and grid lines. They are not explanations, they are questions. Are there elements of your experience of music that map to what you see here? Do the things you like, or don't like, cluster or align? Is this a picture of our world, or something else?]
I needed some Europop this morning. Maybe you do, too. My robots found us some.
Measure my uncertainty about the current function of writing-about-music by noting that although I finalized my 2012 music list by the end of the year, it took me almost two months to get around to even an oblique annotation of it. But here it is:
2012: A Year in a Day
Abridged and unannotated versions are also available:
2012: A Year in a Day
Abridged and unannotated versions are also available:
When a storm approaches, make sure you have sufficient essential supplies: coffee, cereal, soup, the ingredients for making pancake batter from scratch, cats, enough paper to fold a card and an airplane for every person you know, strobe lights, a 5-hour playlist that eventually evolves from frenetic drum & bass into whatever you hope to feel after all of this is done.
At work I have this thing where you can pick any band and pull on the strands that connect them to the rest of the universe of music. This is what happened the other day when I wanted to see where the excellent new Kate Boy song "Northern Lights" took me. Bits of fragile atmospheric sparkly electronica started glittering out and there kind of doesn't seem to be any way to make them stop.
I basically do this kind of thing on and off most work days, and yet nearly every time, even with bands I found this way to begin with, I end up with dozens more interesting songs by bands that I'd never even encountered. I don't think the code is creating the bands out of nothing by its very invocation, but if I hadn't written the code I don't think I'd be so sure.
I basically do this kind of thing on and off most work days, and yet nearly every time, even with bands I found this way to begin with, I end up with dozens more interesting songs by bands that I'd never even encountered. I don't think the code is creating the bands out of nothing by its very invocation, but if I hadn't written the code I don't think I'd be so sure.
In the course of checking something at work yesterday I serendipitously discovered that one of our founders has a deeply personal connection to Christian indie progressive rock, as of course do I. Examples of this sadly unappreciated form can be vexingly hard to find, so I made a little playlist of relevant discoveries to which we can refer if we ever need a spiritual boost. Note that not all of these bands are avowedly Christian, but I think we can agree that this music is doing the Lord's work whatever its ostensible affiliations.
¶ 2012 Pazz & Jop · 15 January 2013
The 2012 Village Voice Pazz & Jop music-critics poll is now out. As has been the case for the last few years, I did the data-correction and tabulation for it. The results of this work are here:
· The Voice's P&J home
· My piece about statistics and analyses
· My massive stastistical hyperindex to the last 5 years of the poll
· My own ballot
The short version: Frank Ocean, Carly Rae Jepsen. If the former of these surprises you, you probably aren't paying attention to music criticism. If the latter, you probably aren't paying attention to pop. Neither of these are life flaws, mind you. But now you have a chance to catch up.
PS: This poll used to be tabulated by people. This is not what people are good at. Eventually computers were employed to help count, but cleaning up the data so it could be counted by computers still took several person-weeks of unpleasant human effort. Then some coworkers and I spent in the neighborhood of 25 person-years building a data-correction and -analysis system with which the whole thing could be done in about 3 person-days. Then Google bought the company for which we made that thing, and shut it down. So I spent about 4 person-days writing a new correction/analysis system from scratch myself, with which this year's poll took about 4 hours to correct and tabulate. Including fixing a few stray errors that the previous system missed.
There is a moral in there somewhere.
· The Voice's P&J home
· My piece about statistics and analyses
· My massive stastistical hyperindex to the last 5 years of the poll
· My own ballot
The short version: Frank Ocean, Carly Rae Jepsen. If the former of these surprises you, you probably aren't paying attention to music criticism. If the latter, you probably aren't paying attention to pop. Neither of these are life flaws, mind you. But now you have a chance to catch up.
PS: This poll used to be tabulated by people. This is not what people are good at. Eventually computers were employed to help count, but cleaning up the data so it could be counted by computers still took several person-weeks of unpleasant human effort. Then some coworkers and I spent in the neighborhood of 25 person-years building a data-correction and -analysis system with which the whole thing could be done in about 3 person-days. Then Google bought the company for which we made that thing, and shut it down. So I spent about 4 person-days writing a new correction/analysis system from scratch myself, with which this year's poll took about 4 hours to correct and tabulate. Including fixing a few stray errors that the previous system missed.
There is a moral in there somewhere.
I work at a music company. Our holiday party involves people who work there performing music. I occasionally make music, but I almost never actually perform it. Very, very close to never.
But I wanted to this year. So I wrote a new song to sing. It's called "The Rules That Govern Hearts". I have a couple weeks to practice singing it, but you can start practicing listening to it right now.
The Rules That Govern Hearts (3:37)
But I wanted to this year. So I wrote a new song to sing. It's called "The Rules That Govern Hearts". I have a couple weeks to practice singing it, but you can start practicing listening to it right now.
The Rules That Govern Hearts (3:37)
¶ What Robots Listen to While They Talk About Love · 25 September 2012 listen/tech
Somewhere around 1992 or so, I had to write my "personal goals" at work for the first time. I put down some stuff about design and usability, which I have long since forgotten, but I distinctly remember that the last item was "Exploit the power of information technology to improve my music collection."
My job, at the time, had nothing to do with music. It did provide me with an internet connection for downloading Gary Numan discographies from Usenet, and it did once send me to a UI conference in Amsterdam, from which I came back with approximately 130 pounds of European CDs. But the connection from what I was doing to what I was listening to remained a little abstract.
But that job got me the one after that, which got me the one before this, which got me to now. The scope of "music collection" has expanded quite a bit over this time, obviously, but in fact I am very literally now paid to exploit the power of information technology to improve the world's experience of music.
The vast majority of things I work on, to this end, involve identification or disambiguation or extrapolation or interpolation. They are corrective or exploratory or contextual. They try to carry you from somewhere to somewhere else, or to rescue you from something squelchy along the way. They try to get computers to understand enough about human cues and contexts and constraints to fill humanless spaces with rough facsimiles of what humans would have suggested if they'd had the time.
But I just did one that isn't so much like this. One of the computed song metrics I've been working on is Discovery, which attempts to quantify the idea of songs emerging. This is a detection metric, not an aesthetic one. It isn't trying to tell you songs you should listen to, it's trying to find the songs that people are in fact starting to listen to more than the established prominence of the artists can explain. Discovery songs tend to be new, but a fair number of songs get their unexpected breaks after they've already been out for a year or two. And once enough people discover a Discovery, obviously, it stops qualifying.
There are a lot of fairly arbitrary thresholds and weights involved in this calculation. How much prominence constitutes critical mass, and how much constitutes overexposure? How old can something be and still be sorta new? And the notion is inherently subjective, of course: anything you've heard a dozen times yourself is no longer a discovery to you, whereas a song 1.3 billion people have been dancing to across Asia every morning for the last 6 months still could be. And there is no way to stipulate correct answers against which this score can be quantitatively tested.
But provable correctness is overrated, or at least sometimes irrelevant. Music discovery is working if you can use it to discover music. And now you can test this particular method yourself. Starting today I'm going to be maintaining an official Echo Nest playlist on Spotify with the top 100 songs of the moment according to this discovery score. It's here:
The Echo Nest Discovery
If you subscribe to it, each week's new songs will get flagged for you automatically.
The songs the robots find cheerfully comply with no particular style or pattern. The set is not coherent in any human sense. It's in rank order, but the ranking logic will not be evident or audible. There will be interminable dubstep remixes next to country laments next to Christian hardcore next to Azorean folk traditionalism. You should expect to hate half of these, and find half of the rest inscrutable. This is definitively impersonal. Somebody, somewhere, liked each of these songs, but we've stripped them of any idea of whom or why.
And yet, the fact that you don't know those people doesn't mean you won't agree with them. I endorse this discovery experiment on the purely empirical grounds that I have personally discovered things this way myself. So, for the record, I'm also going to maintain a personal playlist of what I liked from the robot one:
Treasures the Robots Brought Me
My suspicion, which you're welcome to evaluate for yourself, is that my human picks will be essentially no less random for you than the overall robot list. But if you find something, either way, we both win.
[I'm not sure how the robots win. Probably they win no matter what. If I have a non-musical goal to add at this job, it's probably to keep trying to make sure the human/robot thing is never zero-sum...]
My job, at the time, had nothing to do with music. It did provide me with an internet connection for downloading Gary Numan discographies from Usenet, and it did once send me to a UI conference in Amsterdam, from which I came back with approximately 130 pounds of European CDs. But the connection from what I was doing to what I was listening to remained a little abstract.
But that job got me the one after that, which got me the one before this, which got me to now. The scope of "music collection" has expanded quite a bit over this time, obviously, but in fact I am very literally now paid to exploit the power of information technology to improve the world's experience of music.
The vast majority of things I work on, to this end, involve identification or disambiguation or extrapolation or interpolation. They are corrective or exploratory or contextual. They try to carry you from somewhere to somewhere else, or to rescue you from something squelchy along the way. They try to get computers to understand enough about human cues and contexts and constraints to fill humanless spaces with rough facsimiles of what humans would have suggested if they'd had the time.
But I just did one that isn't so much like this. One of the computed song metrics I've been working on is Discovery, which attempts to quantify the idea of songs emerging. This is a detection metric, not an aesthetic one. It isn't trying to tell you songs you should listen to, it's trying to find the songs that people are in fact starting to listen to more than the established prominence of the artists can explain. Discovery songs tend to be new, but a fair number of songs get their unexpected breaks after they've already been out for a year or two. And once enough people discover a Discovery, obviously, it stops qualifying.
There are a lot of fairly arbitrary thresholds and weights involved in this calculation. How much prominence constitutes critical mass, and how much constitutes overexposure? How old can something be and still be sorta new? And the notion is inherently subjective, of course: anything you've heard a dozen times yourself is no longer a discovery to you, whereas a song 1.3 billion people have been dancing to across Asia every morning for the last 6 months still could be. And there is no way to stipulate correct answers against which this score can be quantitatively tested.
But provable correctness is overrated, or at least sometimes irrelevant. Music discovery is working if you can use it to discover music. And now you can test this particular method yourself. Starting today I'm going to be maintaining an official Echo Nest playlist on Spotify with the top 100 songs of the moment according to this discovery score. It's here:
The Echo Nest Discovery
If you subscribe to it, each week's new songs will get flagged for you automatically.
The songs the robots find cheerfully comply with no particular style or pattern. The set is not coherent in any human sense. It's in rank order, but the ranking logic will not be evident or audible. There will be interminable dubstep remixes next to country laments next to Christian hardcore next to Azorean folk traditionalism. You should expect to hate half of these, and find half of the rest inscrutable. This is definitively impersonal. Somebody, somewhere, liked each of these songs, but we've stripped them of any idea of whom or why.
And yet, the fact that you don't know those people doesn't mean you won't agree with them. I endorse this discovery experiment on the purely empirical grounds that I have personally discovered things this way myself. So, for the record, I'm also going to maintain a personal playlist of what I liked from the robot one:
Treasures the Robots Brought Me
My suspicion, which you're welcome to evaluate for yourself, is that my human picks will be essentially no less random for you than the overall robot list. But if you find something, either way, we both win.
[I'm not sure how the robots win. Probably they win no matter what. If I have a non-musical goal to add at this job, it's probably to keep trying to make sure the human/robot thing is never zero-sum...]