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 pasquier@sfu.ca

Matthieu Macret mmacret@sfu.ca

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

Wednesday, July 16, 2014

The Synthesis Calibration Problem

Target sound

Sound Synthesizer

=Synthesized sound

Input parameters

Automatic calibration of Sound synthesizers

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)

Target

Synthesized

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

OP-1 Synthesizer (Macret SMC 2013)

Mul)-‐objec)ve Gene)c Algorithm

Target

Synthesized

General Sound Synthesis Problem

Target sound

Sound Synthesizer

=Synthesized sound

Input parameters

Genetic Programming - Garcia (DAFx 2001)

Produc)on rulesin had hoc synthesis system

Sound Synthesizer

Synthesized sound

Target sound

Coevolutionary Mixed-typed Cartesian GP

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

–Genetic Algorithm•Optimize Input Parameters

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

Mixed-typed Cartesian GP

• Harding (GECCO 2012)

• Characteristics:–Directed Acyclic Graph–Mixed-typed

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

Gene Input 1 Input 2 Extra Param.

Descrip/on

Output Type

3 control control -‐ Division control

4 signal control -‐ Oscillator signal

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

Euclidean distance between synthesized sound and target sound

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

Euclidean distance between synthesized sound and target sound

Evaluation

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

Pd PatchPd PatchPd Patch

Genetic Algorithm

• 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

Initialize CGP /GA population

Evaluate CGP / GA population

Variate GA population

Select best CGP individual

Variate CGP population

Termination criteria ?End

Clarinet sound

Target

Clarinet sound

Target

Clarinet sound

Synthesized

Clarinet sound

Synthesized

Clarinet sound

Pd patch used to generate the target sound Evolved Pd Patch

Contrived sound

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

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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