automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed...

E N G A G I N G T H E W O R L D

Philippe Pasquier [email protected]

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Matthieu Macret [email protected]

Automatic design of sound synthesizers as Pure Data patches using Coevolutionary Mixed-typed Cartesian Genetic Programming

Wednesday, July 16, 2014

mailto:[email protected]




The Synthesis Calibration Problem

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Target sound

Sound Synthesizer

i1

i2

...

in

=Synthesized sound

Input parameters


Automatic calibration of Sound synthesizers

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Synthesis technique Op/miza/on technique Reference

Addi)ve HMM Heise (AES Conven)on 2009)

Substra)ve Par)cle Swarm Yoshimura (Speech Communica)on and Tech. 1999)

Substra)ve Neural Network Roth (AES Conven)on 2011)

FM Cellular Automata Serquera (Applica)ons of Evolu)onary Computa)on 2010)

FM GA Horner (Computer Music Journal 1993)

Addi)ve GA Horner (Computer Music Journal 1996)

Granular GA Fujinaga (ICMC 1994)


ModFM synthesizer (Macret SMC 2012)

4



4

Target



4



4

Synthesized


More complex synthesizer• VST Instruments with GA (Yee-King ICMC 2011)

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OP-1 Synthesizer (Macret SMC 2013)

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Mul)-‐objec)ve Gene)c Algorithm



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Target



6




6


Synthesized


General Sound Synthesis Problem

7

Target sound

Sound Synthesizer

i1

i2

...

in

=Synthesized sound

Input parameters


Genetic Programming - Garcia (DAFx 2001)

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Produc)on rulesin had hoc synthesis system

Sound Synthesizer

i1

i2

...

in

=

Synthesized sound

Target sound

GPGA


Coevolutionary Mixed-typed Cartesian GP

• Coevolution of:–Mixed-typed Cartesian Genetic Programming•Evolve synthesizer architecture

–Genetic Algorithm•Optimize Input Parameters

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Pure Data• Graphical programming environment for audio, video, and image processing• http://puredata.info/• Popular among musician, artist and sound designers

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http://puredata.info/

http://puredata.info/

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Mixed-typed Cartesian GP

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• Harding (GECCO 2012)

• Characteristics:–Directed Acyclic Graph–Mixed-typed


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0 0 0 5 1 4 5 9 2 10 3 0 2 9 7 3 5 6 0 7 9 3 5 4 3 2 0 1

CGP graph

Pd Patch


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Gene Input 1 Input 2 Extra Param.

Descrip/on

Output Type

3 control control -‐ Division control

4 signal control -‐ Oscillator signal


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0 0 0 5 1 4 5 9 2 10 3 0 2 9 7 3 5 6 0 7 9 3 5 4 3 2 0 1

CGP graph

Pd Patch


Fitness function• MFCC (Mel-Frequency Cepstral Coefficients) in windows of 23 ms

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Euclidean distance between synthesized sound and target sound


Evaluation

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i1

i2

...

in

i1

i2

...

ini1

i2

...

in

i1

i2

...

in

GA popula)on (150 individuals) CGP Popula)on (5 individuals)

Pd PatchPd PatchPd Patch






Genetic Algorithm

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• Binary encoding of chromosome• Binary tournament selection• 2-points crossover• Bitflip mutation


Mixed-typed Cartesian GP• Integer encoding of chromosome• Evolutionary Strategy 1+4• Mutation-only approach

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Initialize CGP /GA population

Evaluate CGP / GA population

Variate GA population

Select best CGP individual

Variate CGP population

Termination criteria ?End

No

Yes


Clarinet sound

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Clarinet sound

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Target


Clarinet sound

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Clarinet sound

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Synthesized


Clarinet sound

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Pd patch used to generate the target sound Evolved Pd Patch

Contrived sound


Stats

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Conclusions• New approach to automate the design of sound synthesizers using Coevolutionary Mixed-typed Cartesian Programming• Synthesizers represented as reusable Pd patches

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Future works• Our methods involves many parameters– Parameter sensitivity analysis

• Extend our system to reproduce not only one target sound but a set of target sounds• Improve our fitness function– Move to a multi-objective fitness function

• Quantitative/Qualitative comparison with previous similar systems

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automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed...

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