>> The last top--the last topic before the people go skiing or go sleeping [INDISTINCT]
is Sean [INDISTINCT] and I'm not the reading type of someone [INDISTINCT] So, this is Structural
Improvisation using Automated Pattern Discovery.
I'm actually a researcher in curious security.
So, Suzuki, take [INDISTINCT] with any of my definitions.
Pretty stiff a little bit easy on it.
So, my claim is that essentially The Remix is pervasive in modern culture.
And so, my kind of loose definition of Remix is that how some existing material and we
subtract, add or adjust it until something [INDISTINCT] emerges.
And so, the percent doing the Remixing plays a lot different roles.
They can back as a sculpture or curator, an engineer and many others, I'm sure that they
can do it.
So, what if the Remixer is actually been the song itself and that's kind of the--so my
philosophical question, I wanted to [INDISTINCT] So, a really informal evolution of these ideas
can be trace with Remix, can be traced back to [INDISTINCT] to generate these random poems
from the bag of words.
And that was a big influence for William S. Burroughs and his literally cut-ups when he
would take-- create system texts and chop them off into words of senses and then rearrange
them to make novel stories.
And of course, the pervasive phenomena, The Remix and modern pop music is hip-hop and
sampling and then taking up one step further into mash-ups.
So, hard--is like danger analysis with his--the Gray Album were you have, backing sounds from
the Beatle's White album and focus from Jay-z's black album or an artist like Girl Talk who's
very comfortable mashing up with vocals of James Taylor with Dirty South perhaps.
You know, it's--So, the idea here is that there is this Spectrum of Structure.
So, we have, you know, random pieces like Burroughs' cut-ups or [INDISTINCT] pieces.
Then we have, you know, some--intentionally, often more predictable techno [INDISTINCT]on
Techno and then maybe in the extreme case, the most predictable using might be an example
would be Cage's 4'33.
So obviously, there's a spectrum of between, you know, totally random music and totally
predictable music.
And so, that's kind of what I wanted to explore in this, is this--the spectrum in between.
So, there's this interplay between, you know, structure and order and chaos.
So, one way, I sometimes think about music is this perceptional information channel.
Out of the Shannon information channel; from the creator to the consumer.
And so, outside of--kind of philosophical points, to most people, both of those extremes
become interesting.
If we're just listening to the same note over and over and over, ad ifinitum or, you know,
releasing something totally chaotic that we cannot hope to predict.
To most people, those extremes aren't super interesting.
So, and researchers is starting to measure that misbalance of familiarity and surprise
kind of maximizes people's appreciation.
So, this has a lot to do with how complex is a song.
And so, there's two components to this.
There's how random is the signal.
So, random, meaning, you know, unpredictable in your concepts of surprise and anticipation,
our expectations and those expectations being violated and also how structured is the source.
So, if we think of a song in the sense of a kind of sarcastic process.
What is the--in term structure, need to express the randomness in that signal [INDISTINCT]
And so, there's dozens and dozens of different definitions of complexity.
So, just go over two here.
So, there's the K-C complexity which is also known as the permissive device the--every
string has.
It's the length of the Shortest Universal Turing Machine Program that produces us.
So, in this notion of complexity, we're kind of associating randomness for complexity.
So, if we have a uniform random bit sting, we can't really compress that.
So, the Shortest Universal Turing Machine program is essentially going to have to store
that string to explosively reproduce it.
So, in that sense, it's a associate randomness for complexity.
So, there's this alternate notion of statistical complexity so, it's--if we want to give up
harder, we need to exactly reproduce the string.
We can produce a new string with the same statistics.
And so in that sense, we're instead associated with structure with complexity.
So here where we have a uniform random string in this sense with high complexity.
Here, it's going to have low complexity because we can essentially have a single distinguishing
machine model of all this like transitions which basically models a coin being flipped
and there--that's essentially the same complexity a totally periodic model.
Okay.
So, what I think is interesting about this notion of complexity is it kind of captures
this notion that I was talking about that between this two extremes, there's this structure
and that leads to pop into more [INDISTINCT] So obviously, this is contingence--I'm kind
of exploring these ideas and how do we actually measure complexity.
So, one way of measuring the complexity is that a signal is changed is [INDISTINCT] rate
and one that's often approximated is by the levels and the algorithms so [INDISTINCT]
by something like Jiza and there's--lots of people argue that that's not really good approximation.
But that's [INDISTINCT] a different time but also the complexity of the source.
So again, if we think of the source that generates these notes as some sort of sarcastic process,
once the complexity needed to produce information of particular entity rate.
So, the model that we're using is this Minimal [INDISTINCT] representation from Statistical
Physics of the [INDISTINCT] machine.
And that can directly calculate the entropy rate h_mu and the statistical [INDISTINCT]
And so, this is what we're referring to at the title of automated pattern discovery.
So, just the kind of unabstracted from its--kind of Statistical Physic usage, it'll be more
familiar to people in machine [INDISTINCT] as basically a special kind of hidden mark
on the model with an edger meaning [INDISTINCT] transitions and we have special states on
counsel states which are [INDISTINCT] classes of histories with the same next symbol prediction
and the number of states in the [INDISTINCT] directly from the data and so, having to specify
this policy--this model beforehand.
And so, these are--in the [INDISTINCT] of infinite data, the claim for Epson machines
is that they're minimally sized often in creative model--the stationeries [INDISTINCT] process.
So, here's two really simple Epson machines.
So, this is a period two process.
We have two states and here we have labels on the transitions.
So, on the right-hand side of the label is the symbol that's being emitted so, zero and
one.
And on the left hand side is the probability of that transition.
Okay.
So here, we just bounce back and forth that generates 010101.
There's a slightly more involved Epson Machine where--basically reproducing binary strings
where there's a restriction that no two consecutive zeros could appear.
So in this thing, we basically have a coin flipped.
We can produce a one or a zero with some bi's.
But if we do produce a zero, where in state B and we have to follow that up with a [INDISTINCT]
Okay.
So that's another simple Epson machine and of course, they could be more involved and
they'll get it much heard than that than you know.
So we're kind of exploring these triads with this very simple algorithm that that we're
calling other Auto-remixes.
So, my idea again is that we allow [INDISTINCT] to remix itself at various levels of creating
there.
So in this work, we focus on beats.
And so the ideas that we researched, they have some wave form, we run in beat detection
[INDISTINCT] and we cluster similar beats together and we'll have a time series and
cluster ID's dnd to infer this Epsilon machine to the time series of cluster ID's.
And then we can use the epsilon machine to generate a new time series, you know, random
walk on the epsilon machine and so essentially we're treating the song as a stochastic process.
The clustering allows us to adjust the amount of [INDISTINCT] So I kind of went over this
already but this is just a really simple example.
So we have waveform, we run a beat detection algorithm on and say we have some sample with
four different beats in it.
And we'll run some clusterings, let's say, just standard k means and we'll group this
first two beats into one cluster and second to beats into a different cluster.
And then we'll infer an epsilon machine from that and we'll do a random walk on the epsilon
machine we'll, sample from this first cluster and we can see a random of four beats again.
We're sampling from this first cluster making you their pick.
It needs one or two for that and this time we chose a two.
Here we have cluster ID one, we can sample either of those two beats and this time we
chose a one and so on.
So we did kind of a shuffled version of the beats and so.
So hang up the limiting cases are we can choose the same number of clusters of our beats of
the song.
And so every beat gets a sign choice on the cluster.
And as we decrease the number of cluster then some beats are going to be forced into clusters
with other beats and so we're going to give to increase the randomness in the outright
and see if you can see as we back off the number of clusters, the number of distortions,
playback proportion of the cluster [INDISTINCT] So, so we can also look at kind of the space
of these Epsilon machines or into the entry rate in the statistical complexity.
And so, so this is a figure from a different piece of work.
The idea is that we could actually run this kind of analysis and look, at say where, you
know, a piece by [INDISTINCT] might be or piece by Beethoven might be or we can also
look at how where the machines for a particular--the same song vary as we change this different
number of clusters or inference [INDISTINCT] as they might diffuse globally around these
kind of semi stable subspaces.
And so just to kind of summarize, the ideas to treat a song is a stochastic process and
allowing the song the remix itself through this automated pattern discovery by technique
based on Epsilon machines.
And so it allows us to explore the tradeoffs between structure and randomness in the signal
and the source.
And we think maybe there might be somehow occasions with this to quantify the perception
of these tradeoffs using information theory.
So just to give a little more detail, all was this done in Python and use [INDISTINCT]
remixes DK or the beat detection.
For the features for clustering, we initially started off using FFTCOefficients and also
tried for wavelets but we ended up hitting the best powerful results using MFCC's using
the [INDISTINCT] For clustering, we started out using the initial--just the simple thing
meets algorithm [INDISTINCT] in Sci-fi and we also tried infinity propagation and [INDISTINCT]
plus, plus and [INDISTINCT] package and ended up using [INDISTINCT] plus, plus and to infer
Epsilon machines, we use the computation of [INDISTINCT] So I actually have kind of a
sample of the song here that was remixed using this technique and so I chose the number of
clusters to be half the number of beats, so roughly half the beats are going to be forced
to be stuffed into a cluster with another beat so we take a random walk on the model,
we're going to get some [INDISTINCT] and because it's hard to kind of, especially with the
keys of music that you don't know, that kind of unrolled epsilon machine to preserve the
temporal structure of the song.
And so what you're mostly going to be hearing here is a lot of [INDISTINCT] preserved [INDISTINCT]
if you get affected of the cluster.
So it should sent you to a journey but, once in a while, you will a sound that is a little,
hopefully [INDISTINCT] So that's it.