Vibrations and how they get to your ears.
Noise for airports is a blog about culture, sound, music, and technology.
You can filter the posts to see just things I wrote or made.
Updated (sometimes) by Nick Seaver.
Nick Seaver: Britney Study #5
Another Pop Study.
For this one, I was playing around with masses of sound—what happens if you play the same song 25 times at once? Britney Spears’ “Gimme More” provided the opportunity, and I provided song over and over, staggered and spread around the stereo field, layering and layering.
I imagined that this might have been the soundtrack inside her head back in her crazy days. (Yeah, more old pop material! This is the hazard with using contemporary source materials, I guess.)
I’m reading Inventing Entertainment, an enthusiast’s history of the player piano, and while it is generally light reading (most interesting for anecdotes), I’m occasionally finding really provocative stuff in it. One such passage, an excerpt from Stanislaw Lem’s His Master’s Voice:
“By chance,” explains the main character, Peter Hogarth, an eminent mathematician, “a program tape for a digital computer might also fit into a player piano, and although the program has nothing, absolutely nothing, to do with music—it might refer to some fifth-order equation—nevertheless, when it is put in the machine, it produces notes. And it might also happen that not all the notes thus produced will be in total chaos, but that here and there one will hear a musical phrase.”
The book is about scientists trying to decipher messages from outer space—to convert them from one form of encoding to another that we can understand. This passage is a great literal example of Stuart Hall’s encoding/decoding model of communication.
In Hall’s model, messages are always encoded by the sender and decoded by the recipient. This encoding and decoding can be sociocultural (like in-group language, or based on common cultural references), and that is Hall’s main focus, but the model also works for technological encoding and decoding. What Hogarth describes in the passage above is the co-dependence of data and decoder for meaning making. The punch card on its own does not mean anything; only in conjunction with the computer does it mean mathematically. In conjunction with the player piano, these holes could signify music.
This idea is actually carried out in Yasunao Tone’s Musica Iconologos. The album is based on two Chinese poems. Digital images of the poem’s ideograms (or perhaps of photos representing the ideograms, I’m still unclear on that) are the equivalent of Hogarth’s computer tape. These binary files, instead of being read to create images, are read as audio data in a different computer program. Through what Hall might call an “oppositional reading,” data encoded for one purpose is decoded for another, creating a screechy, noisy, Fluxus-y composition.
(That’s an embedded Flash player with an excerpt, for you RSSers and Tumblr dashboarders.)
In spite of Tone’s avowed interest in indeterminacy (he was loath to make recordings, since they are so repeatable—this data composition was a way to remove intentionality), the liner notes for Musica Iconologos reveal the human hand that intervened in this otherwise pure replacement of decoding processes:
The sound files produced were very short in duration, averaging out around 20 milliseconds in length. […] My task as the digital editor and sound designer was to uncover and shape the larger sounds that lay within each short 20 ms burst. I accomplished this by expanding the sound to a length that best fit the meaning of the word or picture the sound represented. Then, if necessary, I digitally mixed or merged several projections together to achieve a desired grouping of data, following the structure (word groupings) of the poem and the implicit meaning of the particular word or picture in question. Also, where appropriate, digital pitch shifting was applied to certain sounds in order to reflect the phonetic implications of the spoken Chinese word. It should be noted that there was never any exclusion or repeated inclusion of sounds based on their final result. To his credit, Tone always remained true to the poem’s structure regardless of his personal impressions of the music, and in a sense the sounds were a type of “chance operation” in form, as their final organization was established long before the project began production.
That quotation is from the sound designer for the album, Craig Kendall. Because the sounds produced by a clean swap of decoding software were too short, he had to lengthen them. Beyond that, and quite surprising to me, is the discussion of how pitch and structure was altered for artistic effect. It is rather telling, I think, how apologetic he becomes at the end of this passage, trying to recover the idea that something with so much human judgment can remain a “chance operation.” (Of course, even things that seem more obviously to be chance operations are never really devoid of human influence.)
I’m always looking for more examples of this kind of data music, so if you know of anything cool, drop me a line!
Nick Seaver: Ciara Study #13
This is one of the Pop Studies I worked on a few years ago. I was playing around with compositional strategies based on work by various 20th century composers and my digitally-enabled capabilities.
This piece is based on a short sample from the song “Other Chicks” by Ciara, off of Goodies. I played around with loops of it, transposing them and phasing them against each other. This is one of the resulting recordings of essentially improvised loop-playing. It’s not perfect, but hey, that’s why I can call them “Studies,” right?
A recent story on NPR’s Morning Edition describes a new software system to analyze songs for hit potential. Quoth Music Intelligence Solutions CEO David Meredith:
“[It’s] a series of algorithms that we use to look at what’s the potential of a song to be sticky with a listener,” Meredith says. “To have those patterns in the music that would correspond with what human brain waves would find pleasing.”
Meredith’s software uses algorithms to model how a brain finds “pleasure.” Even putting aside the strange attribution of taste to brain waves (as opposed to just the brain, or—I don’t know—the person?), this model has a lot in it to talk about. First off, the idea that pleasure is caused by “patterns in the music.”
What kind of patterns is he talking about?
Meredith says his software found that hits have certain common patterns of rhythm, harmony, chord progression, length and lyrics.
The choosing of parameters, even seemingly innocuous ones like “rhythm” and “harmony,” is an interpretive move. These parameters define the way the software can “hear,” grouping or separating various songs in contingent ways. Of course the story is vague about how exactly the software parses patterns in “lyrics” or “length”—you wouldn’t want to give up any trade secrets. But let’s take length as an easy one.
How would you incorporate length into an evaluation of a song’s “hit score?” It’s presumably a single number. Is shorter better? Maybe the ideal length is determined by a relationship to the various other scores? How do you extract a hit rating from it? By embedding these questions in software, Music Intelligence Solutions obscures them. The software must have a methodology, just as the music critic would, but since it outputs a score—7.6 out of ten is apparently “good for a platinum rating”—this methodology goes unexamined.
David Bell, of the hip-hop duo the Block Scholars, paid $90 to use it.
“To me, it’s an unbiased validation of your music,” Bell says. “It’s not your family turning around and saying, ‘Oh, you got a great song.’”
The computer told Bell he had a 7.1 — good, but not great. So he went back to the studio and remixed. He got his score up to 7.6 — good for a platinum rating. He could hold his head up.
The software configures musical production in a particular and contingent way. Bell produces a song, the machine evaluates it, he remixes, and the machine reevaluates. In the context of evaluative software, the labor of remixing is a negotiation with the machine.
The assumption is that the machine that turns music into numbers, processes them, and gives back a number, must be “unbiased.” By displacing evaluation from people to “a series of algorithms,” Music Intelligence Solutions banks on the obscuring power of technology: “your family” has bias, but a machine does not. Of course, the workings of the machine are entirely sculpted by bias. Each decision about what matters in a song is tweaked and informed by the programmers. The algorithms do not make themselves, nor do they decide which musical traits are significant.
Objectivity is produced through the use of numbers and software interfaces. Technologically mediated bias becomes objective evaluation. Software like Music Intelligence’s UPlaya propagates a certain view of what “hits” are, and even exceptions to the rule are integrated into a particular musical viewpoint:
It doesn’t surprise New Yorker music critic Sasha Frere-Jones that a computer can predict hits, but he says it can’t predict all the hits. Sometimes, songs come along that don’t fit the mold.
Songs that are popular in spite of their evaluation results (Frere-Jones uses the example of “Da Da Da” by Trio) become songs “that don’t fit the mold.” Now, not fitting the mold is another story about how songs become popular. On one side, we have software to account for certain kinds of success (like “I Gotta Feeling” by The Black Eyed Peas—8.9 out of 10), and on the other, a popular cultural myth about the appeal of mold-breaking. Evaluative software recasts songs that don’t meet its model as part of another cultural narrative.
Is this something to worry about? Probably not. For the integration of taste into business decisions, numbers can be very useful—they turn the the qualitative into the quantitative, with its sheen of objectivity. For listeners, though, I find it hard to imagine that you’ll be seeing ads for “The new Britney Spears: 9.1 out of 10 on the hit scale! You’ll Love It!” Actually, I take that back. You might really see these things, like impact factors on science journals, or prominent magazine reviews on CDs. If they actually use these in promotion, and not just in house, that would be both totally wild and fodder for like a million more blog posts.
The reason posts have been a little light lately (not in number, but in content) is that the thesis season has begun in earnest. A little over a week ago my classmates and I gave our preliminary thesis presentations, making a “public commitment,” to use the words of my thesis advisor. This post is an adaptation/abridgement of that presentation into blog format, as another form of “public commitment” and as a place to direct people when they ask me “So what is your thesis about, anyway?”
The Musical Gorilla: Musicality and Automaticity in Mechanical Musical Instruments
In H.G. Wells’s Tono-Bungay, a woman sees a pianola in a man’s living room. She asks him, “Does this thing play?” He replies,
Like a musical gorilla, with fingers all of one length and a sort of soul.
The research I’m doing revolves around statements like this—evaluations of machines that make music. I’m interested in how the various parties involved with these instruments (makers, marketers, users, listeners, and so on) think of them. Are they machines? musicians? Should their music be evaluated like human-produced music? If not, then how should one evaluate machine-produced music?
Only when you can forget your fingers can your brain be perfectly free. It surely stands to reason, then, that the ready made technique of the player piano sets the musician’s brain free to attend to the purely artistic side of the performance.
-Ernest Newman
The gramophone concerns itself with “sound” in a general sense, reproducing it as it is heard, rather than as it is made (this is why your speakers don’t look like little mouths, violins, pianos, etc., but rather like abstracted ears). The various iterations of player pianos, on the other hand, are concerned with how a particular kind of sound is produced (this is why player pianos generally look like—yes—pianos).
There are a lot of good books written about the early history of sound recording, from all sorts of musicological, historical, and theoretical perspectives. When I wrote my undergrad thesis, I had to grapple with many of them. The player piano, on the other hand, is less thoroughly theorized (probably for good reason—How long has it been since you’ve seen a player piano in person? What about an iPod?).
For me, though, the absence of big, authoritative works is a blessing. The various ideas about sound reproduction from the numerous authors who’ve theorized it can come into play without crushing me under the weight of a thousand master’s theses about the history of recorded sound. Because, on a fundamental level, the work of machines like the pianola (or Pianola) is different. These are not machines that try to replicate and model natural phenomena: these are machines that play other machines.
“Where, in truth, is the non-mechanical musical instrument? […] The anti-piano-player pianist is, in fact, a million removes from mere nature; he would be helpless without the huge box of mechanical tricks in front of him.”
-Ernest Newman
A fundamental idea in the kind of technology studies-informed work I do is that machines are not cold, objective things that go about their existence independent of humans. In their use, manufacture, and physical composition, they reflect the influences of countless actors. This is as true of the piano as it is of the automatic piano-player. The piano represents a certain kind of musical interaction—the pressing of keys to call out notes in specific intervals, the ability to change dynamic level, the sustain pedal. The player piano is a physical reading of the piano’s interface, which in its design reflects decisions about what is “musical,” “essential,” “artistic,” or “automatic.”
So what?
“Everybody is an electronic musician. Some of us push only one button in a performance, and some of us push many buttons.”
-Vladimir Ussachevsky
In the modern musical environment, automated music is basically omnipresent. In addition to mp3 players and car radios, we have music games like Guitar Hero or Rock Band that reinsert physical machinery into the path of sound reproduction. Every song now on the Billboard charts (and elsewhere, for that matter) arises from the manipulation and interaction of machines to produce sounds, and with effects like Auto-Tune, this machinery comes to the foreground in new ways. The player piano plays a formative role in our modern relationship to musical machines (I think); by looking at this example at a historical remove, hopefully I can shed some light on our current music listening and making situation.
While exciting, this is all quite broad now (and the stuff in this blog post is maybe half of what I presented last week), so it will surely contract and expand in various ways and areas many times before I’m through.
Projects drift; that’s why they’re called research projects.
-Bruno Latour
Stay tuned for more exciting player-piano based updates with the thrilling “thesis” tag. (They’ll hopefully come in more manageably-sized chunks after this one.)
Nick Seaver: Fergie Study #8 (video)
From the archives, this video is actually a class project I made last year.
The audio is a shortened version of one of my Pop Studies, which is made from a sample of the intro to “Glamorous” by Fergie. I took that sample, pitched it around and layered it, and then listened to what happened when I used Ableton Live’s time-stretching feature in extremes. Time-stretching is supposed to allow you to change the speed of a sample without affecting the pitch, but beyond small changes, it introduces interesting artifacts.
Since the original sample has no strong beat, I wanted to see how the perceived tempo would change as these artifacts piled up. The distinct beat/melody heard around 2:55 in the video is just the result of time-stretching artifacts.
The video is a one-second sample from the official music video, looped and cut up using Jitter in a patch I put together. Little production side note: because I couldn’t install Jitter on the class computer I used to make this, I had to create a freestanding patch that altered the video based on data in a text file. In class, I just edited the text file to make edits to the video. Here is the whole text file, just for kicks:
0, shift clear;
1, shift 1 2 1;
39, shift 1 2 0 2 1 1;
79, shift 2 1 0 3 3 1;
119, shift 3 3 0 0 0 1;
159, shift 0 0 0 2 4 1;
200, shift 2 1 1 3 3 1 0 0 1 2 4 1 1 2 1;
260, dim 90 60 loop 2;
400, 0;
480, 0;
720, 0;
1200, 0;
1380, 0;
1870, 0;
2000, 0;
2220, 0;
2340, 0;
2550, 0;
This is the score I put together for the Scratch Music orchestra I’ve been meeting up with the past couple weeks.
We went through it last week. I had decided that I didn’t want to provide any kind of interpretation for the group or take an authorly meaning-making role, because I was curious about how people would interpret the numbers.
We ended up coming up with two interpretations as a group:
The first read the numbers as a sort of metric—the drummer played a consistent beat, and in the number of beats specified by the right side of the column, each of us had to play the number of notes specified in the left side. So, a general density of notes could be heard, but nothing intentionally tonal. The persistent drumbeat ended up sounding a little too martial and dirge-like, so we tried another approach.
The second divided the group into two halves who divided the range of their instruments into six. “1” was the lowest you could play (or thereabout) and “6” the highest. One half took the left side of each column, and one half took the right, and we advanced through the rows on a signal from the drummer. Once we got to the bottom right corner, the drummer played a killer solo and we read it backwards, with the halves switching columns. This one was more fun to play, I think, yet still quite cacophonous.
I think someone was taking audio recordings, but I don’t know if/when they’ll be online.
Oh yeah, and the numbers are just arbitrary.
(via Eve Essex)
update: There is a nice variety of other scores to check out over at Eve’s blog.
John Coltrane: “Giant Steps” + “Giant Steps (Alternate Version)”
I recently crammed this together in Ableton. I took the two versions of “Giant Steps” I have on my computer, tempo-matched them, and then extended the one that was shorter so they end at the same time.
You can really hear the structuring principles behind the piece here, both the large scale structure of sections and the patterns within the improvisation.
Live has a hell of a time with non-electronic music, so I had to go in and correct most of the beats by hand; this means that occasionally the drums in the two versions go temporarily out of phase, but oh well! I recommend listening on headphones or speakers with decent stereo separation, because it can get a little muddy in there—I’ve panned the two versions out a little, so it actually sounds (I think) kind of nice through headphones.
update:
A couple images for the visually-minded among you—one of the whole piece, one of the intro segment.
The top track is actually one continuous piece; I just split out the middle section to adjust the volume a little.
bah bah bah bah bahhhh bah bah:
Acousmatic listening is when you listen to a sound without being able to see its source. It became much more prevalent with the advent of sound recording devices (although hearing a noise in the woods is also an acousmatic kind of listening).
Acousmatic music deals with these sounds specifically, imputing a special quality to the sound whose source is obscured, although all recorded music is, to a certain degree, acousmatic.
For the first day of my summer sound class, I put together a quick and dirty collage of sounds we might encounter of the course of the course. These sounds are all at different depths of recording—some are songs released on major labels, some are field recordings, and at any moment, there are likely a few layers of mediation between you and any individual sound. These different kinds of acousmatic sounds (although one could argue that if you see the speaker they come out of, they are not entirely acousmatic) acted as an introduction and teaser for the students.
I had my students fill out a “listening worksheet” while listening to this, to try and write down what they heard. It was interesting to see the kinds of categories they identified: “a bird,” “techno music,” “T-Pain,” “record noise.”
It’s not a particularly good collage, but in retrospect it seems that I managed to cover a lot of what we would end up discussing in it. (It also contains a large chunk of the sound collage I put together about a year ago to introduce myself to my new classmates.)
School starts up for me in a few days.
I’ve been trying to avoid too much metablogging on here, because I have found it to be a black hole that sucks up all my posting energy into posts about posting. But, since I’m about to get a whole lot busier, I wanted to make an effort to connect my coursework to the content on this blog.
That way, work on my thesis might count as work on the blog and (hopefully) vice versa. In the spirit of connecting things together, here is my recently freshened up bio from my program’s web site.
Nick Seaver graduated with a BA in interdisciplinary literature from Yale (2007). As an undergraduate, his interest in sonic media led him to research the relationship between the technology of sound reproduction and social conceptions of “noise.” At CMS, he is studying indeterminacy and control in sound transmission, the role of “skill” in aesthetic judgments, and the history of automatic musical instruments.
His academic work is supplemented by experiments in computer-aided composition that combine experimental music processes with pop music materials. In addition to his work in sonic media, Nick has a longstanding interest in the history of the book, which led him to spend a year training full-time as a hand bookbinder at Boston’s North Bennet Street School.
If any of that sounds interesting to you, I hope you’ll keep checking up on the blog for more updates from my academic work, as the posts here will probably take a little shift from “nifty youtubes” to “thoughts about the piano as an interface.” And, if it sounds really interesting to you, I hope you’ll drop me a line in the comments or through email.
It’s going to be a good year, I hope.
