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Evolutionary sound matching: A test methodology and comparative study

Mitchell, Thomas J.

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Authors

Tom Mitchell Tom.Mitchell@uwe.ac.uk
Professor of Audio and Music Interaction



Abstract

With the ever-increasing complexity of sound synthesisers, there is a growing demand for automated parameter estimation and sound space navigation techniques. Recent research in this domain has focused on the application of general-purpose evolutionary algorithms to match specific types of target sounds. However, it is difficult to establish whether success or failure of a particular match is due to the inefficiency of the optimisation engine, or the limitations of the matching synthesiser. In this paper the distinction between optimiser inefficiency and synthesiser limitations is elucidated with a contrived target test methodology that enables the performance of different optimisation techniques to be measured and compared. The methodology is applied to a Frequency Modulation synthesiser, in order to compare the performance of different Evolution Strategy-based algorithms. The algorithm producing the best results with contrived targets is then used to match a non-contrived acoustic instrument tone.

Citation

Mitchell, T. J. (2007, December). Evolutionary sound matching: A test methodology and comparative study. Paper presented at Proceedings of the Sixth International Conference on Machine Learning and Applications, Cincinnati, OH, USA

Presentation Conference Type Conference Paper (unpublished)
Conference Name Proceedings of the Sixth International Conference on Machine Learning and Applications
Conference Location Cincinnati, OH, USA
Start Date Dec 13, 2007
End Date Dec 15, 2007
Publication Date Dec 1, 2007
Deposit Date Jun 16, 2010
Publicly Available Date Feb 16, 2016
Peer Reviewed Not Peer Reviewed
Keywords evolutionary sound matching
Public URL https://uwe-repository.worktribe.com/output/1023385
Publisher URL http://www.ieee.org/index.html
Additional Information Additional Information : Copyright © 2007 IEEE. Reprinted from Proceedings of the Sixth International Conference on Machine Learning and Applications, Cincinnati, IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of the West of England’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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