[PD] [PD-announce] earGram: concatenate sound synthesis/generative music

Gilberto Bernardes bernardes7 at gmail.com
Wed Nov 6 13:36:29 CET 2013

Dear Marco

thanks for your feedback. The dimensionality reduction algorithms are not
externals...simply abstraction, so, I guess you will not find any trouble
running it on Linux. If you run it on pd-extended, I'm pretty sure you will
not need other any external libraries, with the exception of gridflow for
the computation of eigenvectors in PCA.

Look into earGramv0.18>dependencies>abs you will find all abstractions
here, or just open the file _absOverview.

There's a couple of solutions for dimensionality reduction more or less
complex such as:
self-organised maps (SOM)
random  projection
star centroid
and star coordinates (the only applied in earGram, actually)


2013/11/6 Marco Donnarumma <lists at marcodonnarumma.com>

> Hi Gilberto,
> thanks for sharing your work!
> I'm interested in looking at your dimensionality reduction objects, but
> I'm a Linux user. Any plan to port your work to Linux?
> thanks!
> best wishes,
> M
> --
> Marco Donnarumma
> New Media + Sonic Arts Practitioner, Performer, Teacher, Director.
> Embodied Audio-Visual Interaction Research Team.
> Department of Computing, Goldsmiths University of London
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Portfolio: http://marcodonnarumma.com
> Research: http://res.marcodonnarumma.com
> Director: http://www.liveperformersmeeting.net
> On Sat, Oct 26, 2013 at 1:56 PM, Gilberto Bernardes <bernardes7 at gmail.com>wrote:
>> Hi,
>> I’m working on a project for my PhD that recombines audio segments according to pre-defined generative methods. For those familiarised with concatenative sound synthesis, earGram reformulates the notion of unit selection algorithms to encompass generative music strategies. It relies heavily on timbreID  a known library for PD by William Brent.
>> So, here's the website that hosts the project:
>> https://sites.google.com/site/eargram/
>> You can find project examples in the download section as well. P
>> In addition there are plenty of abstractions that may interest you, in particular clustering and dimensionality reduction algorithms...
>> As you can guess, I would like to get feedbacks and advises.
>> Best,
>> Gilberto Bernardes
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