thank you very much steve. It's a pleasure to
be here. I really, really appreciate your,
your attention. Very nice to talk to a very
different audience. Yeah, so I am as as
steve mentioned a musician or composer and I have used
computers as part of my practice for more years than
I care to mention. Now. Today, I
wanted to talk to you a little bit about the
use of noise in computer music. And in order
to do that, I'm going to first give you
maybe a little bit of context in terms of artistic
practice and also talk a little bit about what noise
is. And then we'll dive into some musical examples
and eventually we will be getting to looking at coach
. I think that's, that's probably what gets everybody
excited around here. Is that right? Little bit
or or or you were overcome Bianca, it's sort
of like post code coding. Excellent. Okay,
well, first of all, there's the question of
noise, what why would you want to even mention
noise in the context of music? Which in almost
any definition of music is some form of organized.
So there are many different definitions of noise floating around
. Some of them are technical definitions. Some of
them are more aesthetic definitions and some of them are
really things that are physical measurements. Some are how
people perceive. So in terms in terms of music
, noise can were even larger context of noise is
sometimes referred to as unwanted sound. Yeah. In
the area of communication theory, it might be simply
sound that's not part of the message. It's And
in that in that sense, technically unwanted, even
though it might be interesting, mm there are further
more value judgment ways of using noise. Like if
somebody doesn't like a piece of music, they might
say, oh, that's that just sounds like noise
to me. So it does have kind of kind
of negative connotation, but at the same time,
in my own creative practice, I found ways to
use noise that seemed to it seems to be kind
of congruent with what I I think maybe some aspects
of our present culture. So I'll get to that
in a bit now, as far as some technical
definitions of noise. Um, I think the main
sort of technical definition of, let's say white noise
is the purest form of noise is just, it's
a random signal that has a constant power status.
So how many of you know what a spectrum at
? Okay, well, Spectra is basically the frequency
contact in some signal. And and since I was
a musician and listen to frequencies, spectrums are very
interesting too. two musicians. So a noise a
noise spectrum would be simply a spectrum that has every
imaginable every natural frequency in it. Now, moving
that from the area of just noise in general to
noise that we can actually play play with on our
computers. There is another definition of noise as a
discrete signal. We're the individual samples of the signal
are uncorrelated. Does does that make sense to people
? Like the difference between something being correlated and uncorrelated
? So the basic idea is that, and you
consider a sign that's a highly correlated signals. Everybody
knows what a sine wave is, right? Something
goes up and down and up and down and up
and down forever. So highly highly correlated signal generated
by a fairly simple equation. And no matter where
you are on that sine wave, you have a
really good idea. Let's say you sample that sign
. They say, what is the number of particular
point occurred? You have a pretty good idea what
the next point is going to be. This is
highly correlated. It's not going to jump around everywhere
. So noise is really the opposite of that.
No matter where you are in the noise signal signal
, you can basically, you have no idea what
the next sample in a discreet noise signal is going
to be. And an interesting thing about that is
that the single could actually look highly correlated that that's
voice some small segments. For example, if you're
just flipping a coin And you flip the coin 10
times in a row and you get heads 10 times
in. Rome, If it's an honest coin,
if every time you have a 5050 chance of being
either heads or tails, the fact that you happen
to get a stream stream of one's is completely consistent
with the idea of uncorrelated signal. Because you still
the next coins, you still point trust you still
have a 50 50 chance of getting either heads or
tails. Okay, now there's one last definition beyond
this idea of a a digital noise signal as a
series of a NCAA related numbers, which is that
in practice the kinds of noise that that one works
with on a computer are pseudo pseudo random noise.
So what that means is that the actual spectrum of
the noise is in fact flat, but it has
to be generated by some algorithm. And so that
means that kind of, paradoxically, on one hand
, the noise seems to be completely unpredictable and it
has it has this sort of distribution of such that
if you're looking at it in terms of outputs,
you couldn't really predict what what the next thing was
going to be. But because it's an algorithm being
run on a computer, in fact, it's a
totally predictable sequence and it's totally repeatable sequence to so
there's that kind of little bit of a wire d
built into working with pseudo random noise, because you're
getting the effect of randomness while using a in principle
and actually practice totally controllable disreputable out. Okay.
Okay, so that's a little bit about definitions.
Now, let me try to put this in the
context of artistic practice. How many of you do
some kind of artwork, whether it's writing poetry or
making films or composing or writing novels. Okay,
and how many of you do not don't think of
yourself as being creative artists. Okay, all the
faculty raised their hand. Okay, so as as
we can imagine, I'm glad you put the word
. Or even even those of you who don't,
our artistic creators are no doubt artistic consumers of some
sort. You all listen to music, you watch
films, read books of books and additional that are
not just technical books. So, so it's it's
quite clear that artistic practice is such a broad thing
that there are many different ways of thinking about.
One way to think about artistic practice, let's say
that you're composing a piece of music and you,
one way to think about that is you're just making
a series of decisions. Okay, so let's let's
say that you're writing a melody and you're thinking,
well, should the next note b a long note
should be a short note. Do I want this
melody to be a, a really kind of dramatic
sort of sort of intense thing? Do I want
it to be relaxed thing? And so all all
those sorts of decisions are made at lots and lots
of different levels in creating a piece of music and
there's a lot of iteration, you might make a
decision and compose a little bit of peace and then
step back and think about it and say, well
actually that melody is pretty terrible. Maybe I should
write a new one. Or you can say,
well, This melody is interesting and I can do
the following 10 things with it that I think would
be enjoyable to listen to. And so that as
a result conditions further decisions. So if you,
if you think about on artistic practice as in this
kind of reductive way, a series of decisions are
made by an artist, then one interesting point comes
up, which is how constrained are you're making those
decisions or on the other hand, how free are
. And the the interesting thing is that sometimes you
might feel that you're free when you actually are being
constrained by forces that you're not even aware of.
So let's say, for example, that you're writing
a Yeah, technically, Well, the odds are
it's going to be in 44 right? Because it
has to work on the dance floor. So you
immediately have certain stylistic constraints. And if you're writing
right, techno track, it probably is not going
to be for strength work because it has to be
played by the DJ. So it's going to it's
going to have to be some form of electronic music
. So just by deciding what kind of peace you
want to write, you already have all sorts of
constraints that are coming into play. And if you
go further back in history, when people didn't have
access to nearly as much cultural information as we have
available to us instantaneously, you find that stylistic constraints
become considerably narrower in a way, so that so
that it becomes a lot easier to talk about common
practices or shared, shared stylistic constraints that affect a
practice of a large number of composers. And that's
why let's say if we were listening to the music
music of Bath, whose basically and early to mid
18th century composer, you here very different kind of
style, A different style, let's say then if
you're listening to a mentally 19th Century Proposal Like Block
. Okay, so another way to think about constraints
is how you actually make and use it and there
you could you could make a a sort of a
continuity between on the one hand, just freely making
decisions only constrained, let's say bye style or doing
the kind of composition. That's actually much more rule
based that actually has a kind of algorithmic aspect to
And how many of you were musicians. Okay,
so for those of you who are musicians, can
you think of any any form of music, any
kind of music music that is very strictly rule based
You can take 12 argues, you can do what
gratifying loose exactly. That is a beautiful event.
So you have a series of chords and and that
series is basically a fixed form that you have to
work with. And then of course, within that
form, there's any number of ways that it can
be expressed, but it's an algorithm. I mean
if you do a 13 are Sequence all of a
sudden is not 12 lose it has rules. Another
example would be a cab That's sort of like the
song three blind mice where you just you basically using
a melody and then somebody else sings exactly the same
melody at a delay and so forth. And it's
composed in such a way that that the piece builds
up harmoniously, but nonetheless, there was a very
strict constraint there there's a very strict, I'll.
So, in that way of thinking about music,
you really kind of have a a story of greed
from highly constrained ways of composing. Too much freer
ways of composing. And the interesting thing is,
even in the freer ways of composing, you still
ultimately have some constraints coming from your own personal habits
, especially after you, after you compose for four
years for decades, you start doing the same things
over and over again. So, in that,
in that context, it's useful to have compositional techniques
that can pull you outside of stylistic constraints. Mhm
. Your own personal habits to get you into other
areas to get you two kinds of musical expression that
you might not have gone to without without using these
techniques. So, in a sense, noise could
be used as a way of disrupting some existing patterns
that might get you to a different kind of music
. Okay, so what I'd like to do now
very quickly is before getting to the actual deployment of
noise is give you a little bit of historical background
of a few different techniques that, Well, they're
not exactly noise techniques. They lead to certain unpredictable
results. And one of the first one is the
14th century practice of ice arrhythmic motet and that's that's
quite interesting approach to music, essentially, when we
think about music in terms of melody as as a
central feature, the melody is a is a sort
of an indestructible unit of a combination of pitch patterns
and rhythms. They go together. Now in later
in development, you might try different, different patterns
associated with those, but the melody itself as a
unit really puts these things together. So the interesting
thing about the ice arrhythmic attack is that it separates
out rhythm and pitch contours. And very often what
will happen is there will be a melody with no
rhythm and rhythms that repeat at various at various time
scales. So the result is that you get some
rather complex music and also once you start this process
off, let's say that you come up with some
sort of pitch melody and some sort of rhythm,
melt Melody, you couldn't necessarily predict in advance without
working through the algorithm what the results will be.
And you get these very interesting permutations. So,
I'd like to very quickly play you an example of
a piece by the 14th century composer, Philippe de
Vitry. He's part of the ars nova uh Movement
in the early 14th century France. And so we
would like you to be aware of is the,
just the complexity as a result of what is essentially
a musical algorithm where you are playing out pitch pitch
structures and rhythm structures that are not in line.
Oh, okay. I want him to play enough
of that for you here in the sense of how
it plays out. And this to me is a
really good example of how a compositional technique creating to
a kind of music that you might not close into
it. If you were just sort of strong son
towards on allude and singing along with it, you
would wind up with a much simpler kind of music
. But what's interesting here is, did you notice
how some parts of the songs are moving very fast
and some parts of moving very slow? It's a
very interesting, intricate thing. I mean, The
14th century was really very interesting, kind of an
avant garde period in in french and and other european
areas of composition. So I want to jump now
ahead. That that's an example of a an algorithm
which is relatively deterministic. It's not completely automatic because
the composer then decides which, you know, how
long to stretch these things out, how to combine
the different elements to get harmonic relationships that you now
, I'm going to jump ahead to another kind of
composition which is again not noise based, but which
seems to take you to the edge of noise.
In the early 20th century, there was a technique
that was developed in the post tonal period called 12
tone music. And the idea talked on music was
that you would take all of the possible notes,
all of the chromatic notes of the of the european
musical scale and put them in a particular order and
then use that ordering of those notes as the basis
for entire piece. So in a way that was
a much tire constraint than the way music was often
composed prior to that. So it was an algorithm
to tighten up the compositional procedure. But the result
was to get the kind of music which sounds nearly
random. Now, what I'm going to play you
a little bit of is a piece by a french
composer, he's still alive. His name is Pierre
boulez And this is a piece from 1952 called structures
or structures. And it takes the idea of a
fixed structure that is employed over and over again to
pitch. And like the ice arrhythmic attack of de
Vitry employ applies it to other aspects of the music
. So in addition to applying it to pitch,
he applies it to register. Is it a high
note? Is it a low note? He applies
it to dynamics? Is it loud? Is it
quiet? He applies it to articulations. So very
much like an ice arrhythmic motet. The idea of
the coherence of a melody is completely split apart and
instead you have all of these structures that separately are
imposed to create an accurate result. So let's listen
to a little bit of struck tours snake. Mm
Yeah. Mm. Now, the interesting thing about
that, did that, is there anybody who didn't
hear that as having a large amount of randomness in
it? So the interesting thing is very much like
pseudo random noise, that score was totally determined by
algorithms. There was actually almost no composer intervention.
The composer intervention was basically about making the rules and
once the rules were made that music is what came
out now. Uh huh. About the same time
, there was another area of research which was in
the area of really trying to create a stochastic music
, a kind of music that had algorithms but those
algorithms were driven by noise. And so I'd like
to play you an example of what is considered by
some, to some people to be the first computer
using this is the Iliac Suite, which was which
was created by a composer named jerry Heller. Uh
he was a chemistry professor working at the University of
Illinois in Urbana Champagne. And you'll hear that there's
a certain amount of composer intervention in the style,
but the overall structure of this is generated by the
application of noise to a set of rules to generate
music. So this is this is algorithmic music with
noise coming in and at the top end. Okay
. Okay. Yeah. Yeah. Did that sound
gravitating? It sounds highly structured. And it's a
really interesting thing that, you know, like so
much about music. On the one hand, we
have technical definitions of what we're working with, The
technical definition of white noise. But on the other
side we have how we actually perceive these things and
very often there's there's tension or real dissidence. So
on the one hand you have, we heard the
peace bible s which was totally determined and highly pattern
sounding totally random. And on the other hand we
have the killer which was very largely ran in how
it was assembled. But with some rules to force
that those random decisions to sell musical and we just
don't hear it is rand. So I think that's
very interesting. And I will be getting shortly to
my own compositional practice, ensure you showing you what
I actually do that. Now I'd like to Mhm
. Now move to some actual code and so yeah
, actual code here. So what I'm showing you
now is what is, in my opinion, one
of the most interesting computer music languages. It's a
language called Supercollider. And uh it's interesting parts important
because it actually really is the language it really is
a computer music language. Where as some of the
other computer music languages, something like C sound is
really a little bit more like assembler code. Uh
And another program that's very popular, which we'll see
in a bit called maX MSP, is a visual
data float program. And as you probably know,
actually coded with data flow programs can be incredibly frustrating
when you just want to write a line of code
to say exactly what you want to have. So
what supercollider is essentially is a sort of a mix
of a sound synthesis language that generates audio samples.
That is controlled by a high level language which will
have a lot of features that will be familiar to
you. It has things like like generations and functions
and variables. Okay, so let's start with noise
and just how we perceive it. So one way
the easiest way to create sound in Super Collyer is
to create a function and evaluated by sending a message
place. So inside here I'm going to write a
very simple program and executed. So one thing about
supercollider is it is totally object oriented. Every everything
in supercollider, including numbers, they're all objects.
So for example, if I type the number five
and evaluate it, I'm not going to show you
exactly how I evaluate it. It just evaluates to
five, but I can send it the message,
reciprocal And it will evaluate to 1/5. So it's
a really convenient sort of interpreted language that's that's a
lot of fun to play with. Now, what's
happening here is white noise. Is an audio object
is an object that will generate audio samples if you
send the right message and dot A. R.
Is a message to tell this object to start generating
pseudo random noise at the audio raid. And this
one parameter in here is a an amplitude value just
says multiply the output by a certain amount. So
if I evaluate this, then we get white.
Yeah, I could also just take this part out
and just multiply 5.1 and we should be only getting
in one speaker. So if I, another nice
thing about supercollider is involved multi channel expansion. So
if I want Rather than one channel, 128 channels
, which might be useful for the cube. As
a side note how many people are aware of the
existence of the cube at the Center for the Arts
. Okay. It's a very cool place that has
Upwards of 128. So super flyer is really the
language to use. But if I expand to two
channels then stones out now that is going to just
generate a sequence of white noise and it has a
kind of a sound to it. If I if
I just let it run for a little bit,
maybe I should just play a little bit louder there
. Mhm. Mhm. Does that sound more or
less uniform to you? It has a sound more
or less. I mean it's not exactly 100 uniform
, but it's sort of sort of comic a little
bit like maybe maybe fan noise or or the sound
of a breeze. But it's definitely this kind of
ambiance signal, Interesting thing is remember what I mentioned
about, you could be flipping a coin which is
a random signal and you might wind up with 10
heads in a row. The interesting thing is if
we actually mhm capture a small amount of sound of
noise, actually hear patterns and the noise, which
is kind of interesting. So what I'm, what
I have here right now is um I'm going to
create a buffer and buffer is just an array that's
going to store a series of numbers. So that's
what this thing is and I'm going to store basically
, let's say one second worth of this. And
now what I'm going to do is I'm going to
record into that buffer and we're not actually hearing anything
, but that was more than a second. So
if I wanted to I could go ahead and plot
this buffer and you can see there there is that
noise and we're not I don't know how well we
consume in here, but you can see this is
very spiky signal, there's really no no correlation from
one sample to the next. That is evident even
though this is was generated by a totally repeatable algorithm
. So now what happens if I play that noise
as a loop? Does anybody? Yeah, Okay
, now let's let's even even shorter here. So
instead, what I'm gonna do is make this buffer
size, let's say 200 milliseconds and then we reported
to it and then we'll play you're going to hear
that. And we can actually if we took it
inside of the make it short enough That the repetition
would go above 20 Harris would move into human audio
, we would actually hear this as a pitch and
I'll demonstrate that very waiting for you. Okay,
so we're still listening to noise. I mean if
we wanted to plot this thing thus, that's what
we're hearing. What we're hearing is that repeated over
and over again. And it gives us the perception
of pitch because we hear the larger pattern of repetition
over there. Okay, now let me give you
another example of noise here in the sense of pattern
. So what I have here is a bunch of
code that I'm going to gloss over really quickly but
supercollider as a full fledged computer music language allows you
to read in sounds you might have lying around your
hard drive and I do happen to have a bass
drum and snare drum want line wrong. And then
these symptoms definitions are basically ways of creating audio synthesis
graphs that can be repeated over and over again.
So, really nice thing about super flyer. All
right. Is that in a total pattern of synthesis
of sex can be made as a bunch of individual
notes that you can have a lot of control.
So this is this is creating some structure and 11
structure is just this place sound thing, which is
going to just play a sound in a towel supercollider
to play. And the other thing is what I'm
calling acid because is anybody familiar with acid house music
a little bit. Okay, so acid has a
lot to do with the idea of a tone that's
rich in harmonics that you sweep a filter over so
we can acquire that kind of self. So that
being said, I could haven't loaded this sound.
I can play it as much as I wanted to
and if I wanted to test the acid sound.
Sure. Okay. Yeah, yeah. So that's
that kind of sense of having a filter sweet down
on looks like a sergeant were. Okay. Now
, the next thing is going to be very similar
to what I did with the, with the noise
. I created a pattern, a random pattern and
then just repeated it over and over again. We
heard that pattern as as meaningful in some sense.
So the next thing I'm going to do is use
a few more patterns. And so this thing over
here, I think this time smaller, is this
still legible? More or less? That might be
mhm. Well, I can always go of and
illness. So this is a little bit more like
actual supercollider codes just generate things in real time.
And so what I'm doing is I'm setting a bpm
beach permitted as 130 and then I'm calculating an actual
beat as the actual duration beef is 60 Because if
we're looking at seconds bpm for 1/2 per b would
be 60. Okay, so essentially we get a
beat that's going to be somewhat faster than a second
, and then I divide to get the A 16
Or rather a quarter to be divided by four,
and this duration is just how many seconds to go
in. Now. A routine is basically a uh
a sequence that is able to be paused or and
or continue. So, so if I wanted to
, let's say regularly play beef, which is what
one often does in techno, all you have to
do is play a note, wait while play another
note, wait while play another note, Wait awhile
. So this routine basically does that. Mhm.
So then for the, the bass drum, I'm
going to now, the cool thing for those of
you who are fans, I can just comment our
code that I don't want to actually execute. So
I'm gonna comment out mm few of these routines and
then I can execute this part here. Okay.
Okay. So that's that is pretty simple code.
But what's happening here is remember that since definition that
I made a while back to play a noon this
place sound is just playing what's in the buffer of
that base drop. And I could replace that with
anything else. So you could think of supercollider potentially
as a drum machine that could have 1000 different drugs
out. That's that's the view of your program.
And all we do is we play the sound,
we send the message, wait two the being and
then I use this kind of updating of how far
we've gone in time just so that I can stop
them. We've got to a certain point because this
is a classic, wildly. It just says,
wow, now is less than the duration we run
this thing. Okay, so let me add in
another element. This is the snare drum has its
own pattern here. So now the patterns themselves are
global. So if I wanted to I could change
the pattern is being read, go for it.
Okay, now all of that is totally deterministic.
Let's add in the random part of this now,
which is essentially to take uh to fill an array
of 16 beach because we'll have Basically 16 16th notes
through the bar with a random pattern each and these
are frequencies, Each frequency will be between 100 and
500. Yeah, so I'm gonna comment out now
the drunk part of this and just do the bass
part every time I do this I'll get a different
pattern. Okay, now, if I really wanted
to keep things static, this is this is the
beauty of of the sort of pseudo random noise.
Uh huh. Each each of these routines has its
own thread so I can say this thread Grand C
equals some value. So did I? Very interesting
. Um I couldn't tell you why that was I
think I might have to put it outside routine but
we'll give we'll give that a try here. Um
see that you can hear that that's that's not painted
and no matter how many times I try this,
that particular random sequence is preserved. Okay, so
now let's bring the whole thing back in and then
you will get a kind of an acid uh results
and then I could change other aspects of the synthesis
. Okay, so you can see that this is
this is a combination of straight, straight up procedural
programming and real time sound. Some possess that you
can interact in a bunch of ways for the main
point is that we're using noise as a way of
getting two different kinds of results. So let's say
that I take out this RAMsi thing, I just
let it be whatever is going to be okay,
you can hear that's different every time. Great.
Yes, we're last. So I mean one of
, one of the classic definition of insanity is doing
the same thing time and expecting different results. But
actually that's exactly what we do when we're doing algorithm
. Mark, you have you have an algorithm.
But the introduction of noise means that the results actually
are going to be different every time you do the
same things. And yeah, just one question can
be applied to rhythms as well. Then you apply
rhythms in order to sort of. Absolutely yeah.
The thing is because you guys know programming can control
anything you want. So, so where I'm just
having this weight thing via costing, it could be
a random value. I could generate random patterns.
I could generate groups of patterns that repeat over and
over again. So, so yeah, the sky
, the sky is kind of limited. Now,
I'd like to look at a couple of other examples
really quickly of the application of noise because what we've
seen here is kind of very literal minded one.
But I also, in my own work tried to
use noise as a way of guiding other kinds of
procedures. So you'll recall that the, um,
that the killer that we heard was largely regimen and
, but did not sound right. So I'm going
to show you another example of that. This is
a piece I wrote in 2011 called Noise trip deck
, which had a few different ways of approaching the
composition with noise. So it's for two islands.
I'll play you a little bit of it here.
It yeah. Mhm Yeah. So does that sound
totally around you? Okay, so here go ahead
, defeating explaining evolving down which keeps flowing order,
which uh, they keep getting slightly slightly like there
are slight changes. But actually what I'm doing here
is something I call articulated noise, which is to
have to work in combination with the computer. So
there is some super flyer code here, which,
which I'll show you very, very quickly, a
bunch of different functions that do different things. And
as I'm proposing essentially this first, this, this
first thing has had to do with different kinds of
melodies That I would use. one was from a
book of north american folk songs. The second source
of melodies was a book of plain chant melodies,
Ecuadorian Chant melodies that go back to the 8th and
9th century. And the third was melodies that I
would have to make up myself. So as I
was composing section by section, I would just execute
on a very simple piece of code. And the
first thing that happens is doing anomaly. Because I
had built into this, the idea that whatever the
structure was, I might well composing, just ask
myself to do something that had absolutely nothing to do
with what else was going on in the piece.
But it gives me some other guidelines or rules.
So it says, okay, this this bar is
gonna be a 54 bar and then I have a
transition. Yeah, in terms of how I moved
to other things. So let's try this again.
A source melody is make it up. So that
means that I have after, in that case,
make up an original um kind of melody. It
tells me about the articulation, tells me how long
it should be and how I transition. So as
a result of this composing through this piece, some
melodies moved gradually to the others, some instantaneously.
And there's a huge amount of randomness at the high
level structure, but at the lower level structure,
I'm making decisions in a very, very determined fashion
. Uh I'll give you another example of this which
is the noise in prompted. Now the noise and
prompted is a guided in private improvisation. And now
we are going to see max MSP, which is
another, another kind of language. And the way
this works here is that basically the two violinists have
to two laptops, they're connected together over wifi,
and one of them essentially executes code to create a
piece that will guide their improvisations. So every time
they do this piece, the guiding of the improvisation
is going to be different and then they actually play
it in real time. So first of all,
I'll play you a little bit about what that sounds
like. Mhm. Mhm. Yeah. Mhm.
Mhm. Yeah. Yeah. Mhm. Mhm.
Mhm. Mhm. Yeah. 13. Mhm.
Right. Oh yeah, I just wanted you to
hear enough of that here. They're improvising. But
could you all hear that? There are different sections
where they're different improvising in different manners. So I'll
show you a little bit of what they're actually seeing
. And again, this is this is basically a
noise guided improvisation. So what's the starts? One
computer will communicate to the other. And so you're
seeing the two parts. The first violinist would only
be following this part in the second, this one
. So you could hear that. Then basically the
instructions are if one violence is going completely crazy,
the other has to follow. Some of the instructions
are the same so that this was at the beginning
occurred. These really scratching things that was that particular
instructions and the durations are totally grand and the sequencing
. So this person had to do what they're doing
. Mika is actually a reference to a piece by
Yannis Xenakis that involves lots of lasagna. So it's
a sort of shorthand, the person what I want
you now, this is funny, but one has
to go playing extremely quiet and the other person has
to go completely mental on that. So, so
this is a really interesting for in terms of guidance
improvisation, because just as much as composition improvisation,
even though it's very free, is very heavily conditioned
by stylistic constraints, habits what you can do on
the instrument. So if you have something like this
where you have the computer making random choices about how
each person should play and they're related to each other
. You might be forced into a really unusual situation
. And the structuring of the piece will be very
different than an improvisation that that's completely free. And
sometimes you get these really interesting things where 11 violinist
is instructed to imitate what the other violinist is doing
and the other violence at the same exact, you
know, instruction. So they're all listening each list
, trying to figure out what the other violinists playing
. It goes into this kind of uh you know
, an infinite loop that way. So this this
is an example of how you can you can use
noise in the context of applying basically computational thinking.
Uh huh That that was that's really great. Mm
But anyhow that did bring me to pretty much what
I want to share with you, which is the
idea that, you know, artistic practice can very
easily today incorporate computation. But also the idea of
noise is a very valuable input to artistic thinking that
can get you to places that you wouldn't necessarily be
able to get to by yourself. So thanks very
much for having me. And I'll be happy to
answer questions. So I guess the question is this
a turing test? Well, the turing test already
had, which is that the that random folk noise
piece that I played? I played it for,
you know, large numbers of people and yeah,
They hear his music. And then I say and
by the way that piece was 80 random and they
go what? Well, But is that because you
but a musician extra experts? Well, as I
said, I played it for musicians, you know
, in other words. And this is a really
interesting question. There is actually some research having to
do with how well we here. So because a
large amount of our theory about total music in the
european common practice has to do with the argument for
coherence, that you can just take one piece of
music and chop it into little bits and rearrange it
because there's a narrative structure and further all a total
structure, which is which almost looks a little bit
like uh like chomsky's linguistic analyses of spoken language.
So that shouldn't work. But there was some there
was some research back in the late 90s where they
sure no listeners who have been exposed to classical music
took a Mozart symphony, chopped it into individual sections
and rearranged in ways that were plausible and most people
didn't notice the difference. So I actually think that
you know the kinds of structural hearing our ears today
and our experience of culture I think is just completely
reprogrammed From what it used to be in the 18th
or 19th century. So we just don't seem to
be responding to that as much as we respond to
surfaces. So he's like this which is very plausible
in terms of its individual elements. And as you
notice in the coding pays a lot of attention to
how things transition, which is something that people do
notice resulted in a peace that if I can tell
people that it was random, nobody would have guessed
. So. So I mean that seems to be
about as close to the turing test if you're talking
about like can you tell the difference between algorithmic music
or music created by computer computer? But the funny
thing is, and I actually just posted this to
my twitter feed because I was just in a mood
. So I said that, you know, in
the 20th century you say isn't libraries and memories,
right. I don't know very many people are old
enough to remember what that means. But it's like
if you're listening in a room, could you tell
the difference between a live person singing or is it
memorex the team good enough. Is reproduction that you
could not hear? The difference between something being played
back speakers or played or some live All right.
Today, I think the question is, you know
, if you read a news article, was it
written by a human or written by a computer?
Because there's a big thing about how computer programs are
getting pretty good at writing news articles. So that
idea of distinguishing the human from from the the computer
program, I think it's getting hard, let's say
the computer programs are starting to get better and better
at fooling us. Yes. Since I I just
wanted to know, I've been a lot of times
feel like I can't describe what I feel is when
I go back. Yes, it's hard to describe
. But then going back here, does it again
bring me to the point that what I see as
speed is really is a condition of the programming of
my brain that has they just learned over time.
And then uh so it is just algorithm, is
there nothing we feel at all? And it's just
that question for you, there was definitely a sound
of algorithmic music. I mean if you sort of
make a lot of random decisions and you know,
there's there's a there's a feel of that, I
mean just as the feel of that sort of machine
regularity of that's not a human rhythm, it's an
inhuman rhythm. But we seem to like it.
You know, maybe the same reason people like watching
terminator, you know, there's, you know,
the humanness of it has a certain kind of appeal
to but and I think we're probably going is really
bridging that gap. And yeah, I mean the
second you put on glasses, your cybernetic organism sense
. And so I think that kind of integration of
technology is just going to keep on, keep on
going. And of course, yes, there are
lots of programs that you can program feel into the
drum machines do other questions, speaker speaker