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Convention Paper

A Comparison of Parametric Optimization Techniques for Musical Instrument Tone Matching

Authors: Roth, Martin; Yee-King, Matthew

AES Convention 130 · Paper 8418 · May 2011

Abstract

Parametric optimisation techniques are compared in their abilities to elicit parameter settings for sound synthesis algorithms which cause them to emit sounds as similar as possible to target sounds. A hill climber, a genetic algorithm, a neural net and a data driven approach are compared. The error metric used is the Euclidean distance in MFCC feature space. This metric is justified on the basis of its success in previous work. The genetic algorithm offers the best results with the FM and subtractive test synthesizers but the hill climber and data driven approach also offer strong performance. The concept of sound synthesis error surfaces, allowing the detailed description of sound synthesis space, is introduced. The error surface for an FM synthesizer is described and suggestions are made as to the resolution required to effectively represent these surfaces. This information is used to inform future plans for algorithm improvements.

Details

Published in
AES Convention 130
AES Convention
130
Paper number
8418
Publication date
May 6, 2011
Session subject
Audio Signal Processing and Analysis
Affiliation
Goldsmiths, University of London, London, UK; Reality Jockey, Ltd., London, UK (See document for exact affiliation information.)
Type
Convention Paper