[ml] Meeting Notes: Generative Music, Music identification, Restricted Boltzmann Machines, etc.

John Hurliman jhurliman at cull.tv
Thu Mar 29 02:05:36 PDT 2012


I can't make tomorrow, hopefully next week though.

On Thu, Mar 29, 2012 at 1:04 AM, Mike Schachter
<mschachter at eigenminds.com>wrote:

> I'm not gonna be around tomorrow night - maybe Gershon and
> others will?
>
>  mike
>
>
> On Thu, Mar 29, 2012 at 12:12 AM, xone <xone at fromthegut.org> wrote:
> > This looks like it was a really productive meeting..  When's the next
> meet up?
> >
> >
> > On Mar 24, 2012, at 10:17 AM, Ian Esten wrote:
> >
> >> Hi all,
> >>
> >> It sounds like the goal of the project is generating new music based on
> a seed piece (or maybe a training set?).
> >>
> >> If that is the case, I would recommend adding a couple of extra steps.
> As part of step 3, I would add additional feature detection to extract the
> musical structure of the audio. Features could include onsets, key
> detection, chords, etc. The generative music step would operate on this
> data and this would be resynthesised to make new music.
> >>
> >> The motivation for these extra features is that without them, you are
> generating new music based mainly on spectral data. The end result is
> likely to be very similar to the source and will likely have the same
> musical events.
> >>
> >> If this sounds like a good suggestion, libxtract would be a good tool
> to take a look at.
> >>
> >> Looking forward to join in with this!
> >>
> >> Ian
> >>
> >> On Mar 23, 2012, at 10:17 PM, gershon bialer <gershon.bialer at gmail.com>
> wrote:
> >>
> >>> == Generative Music
> >>> We discussed making something for generative music. I'm going to try
> >>> to start something with this, and I should push this onto my github.
> >>>
> >>> This should involve:
> >>> 1) Get the raw audio data from a file into a waveform
> >>> PyMir (see https://github.com/jsawruk/pymir does this) does this
> >>> calling ffmpeg (see
> >>> https://github.com/jsawruk/pymir/blob/master/pymir/audio/mp3.py).
> >>>
> >>> 2) Get the spectogram
> >>> Basically, we just take apply a hamming function (see
> >>> http://en.wikipedia.org/wiki/Hamming_function), and then do a Fourier
> >>> transform. This gets the signals from the sound, and  You can see this
> >>> in action at
> https://github.com/jsawruk/pymir/blob/master/pymir/audio/transform.py.
> >>>
> >>> 3) Additional pre-processing
> >>> Some choices are
> >>> a) MFCC (see http://en.wikipedia.org/wiki/Mel-frequency_cepstrum)
> >>> b) Linear Predictive Coding (see
> >>> http://en.wikipedia.org/wiki/Linear_predictive_coding)
> >>> c) NMF (see http://en.wikipedia.org/wiki/NMF)
> >>> d) Something better?
> >>>
> >>> 4) Fit the music to some sort of model for generating the music. The
> >>> idea is to predict s_k (pre-processed sound at time k) from
> >>> s_{k-1},s_{k-2},..s_{k-l} with some lag.
> >>>
> >>> 5) Apply the generative model from step 4 to generate music
> >>>
> >>> 6) Invert pre-processing steps to get a new waveform
> >>> This may or may not work very well.
> >>>
> >>> == Music Identification
> >>> Another interesting project is echoprint. The echoprint project (see
> >>> http://echoprint.me/) has code for fingerprinting music. This involves
> >>> some sort of preprocessing and then binning. It might be interesting
> >>> to improve this.
> >>>
> >>> The relevant code seems to be at
> >>> https://github.com/echonest/echoprint-codegen/tree/master/src in the
> >>> SubBandAnalysis and FingerPrint classes. The sub-band class seems to
> >>> create a time series of the amplitude of various frequency bands. I
> >>> think the FingerPrint class quantizes this data, and applies
> >>> MurmurHash. If someone has a better understanding of this, let me
> >>> know.
> >>>
> >>> == Contributing
> >>> === PyMir
> >>> It is at https://github.com/jsawruk/pymir and numpy and other python
> libraries.
> >>> Things to do:
> >>> * Add more audio pre-processing functions (MFCC, NMF, LPC, etc.)
> >>> * Improve documentation
> >>> * Add better unit testing
> >>> * Better visualization of audio (this should be fairly easy with
> pyPlot)
> >>> * Direct bindings to the FFMPEG api
> >>> === A new library for restricted Boltzmann machine deep learning
> >>> The idea would be to create a new C/C++ library for doing deep
> >>> learning. Theano has some capabilities, but it isn't as fast as it
> >>> could be, and it requires a CUDA Nvidia GPU to be fast. Presumably,
> >>> this would follow the ideas of http://deeplearning.net/.
> >>>
> >>> For linear algebra operations, we could use Armadillo (see
> >>> http://arma.sourceforge.net/), or Eigen (see
> >>> http://eigen.tuxfamily.org/index.php?title=Main_Page).
> >>>
> >>> Boost (see http://www.boost.org/) might be useful for its
> >>> pseudo-random number generator (see
> >>> http://www.boost.org/doc/libs/1_49_0/doc/html/boost_random.html) and
> >>> possibly other things.
> >>>
> >>> You can look over how this is done with Theano at
> >>> http://deeplearning.net/tutorial/.
> >>>
> >>> == Upcoming conferences, contests, etc.
> >>> We are looking at entering PyMir in the ACM Multimedia conference in
> >>> Japan (see
> http://www.acmmm12.org/call-for-open-source-software-competition/).
> >>> I understand there are some other local conferences relating to this
> >>> stuff. If you have details, please send them to the list.
> >>>
> >>> == Next meeting
> >>> When does everyone want to meet next?
> >>>
> >>> ---------------------
> >>> Gershon Bialer
> >>> _______________________________________________
> >>> ml mailing list
> >>> ml at lists.noisebridge.net
> >>> https://www.noisebridge.net/mailman/listinfo/ml
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