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S. Wun, and A. Horner, "A Comparison between Local Search and Genetic Algorithm Methods for Wavetable Matching," J. Audio Eng. Soc., vol. 53, no. 4, pp. 314-325, (2005 April.). doi: S. Wun, and A. Horner, "A Comparison between Local Search and Genetic Algorithm Methods for Wavetable Matching," J. Audio Eng. Soc., vol. 53 Issue 4 pp. 314-325, (2005 April.). doi:
Abstract: Picking spectral snapshots from an original tone is a successful approach to finding basis spectra in wavetable matching of musical instrument tones. Spectral snapshot selection usually requires optimization by a sophisticated technique such as a genetic algorithm (GA). GA selection is compared to a straightforward local search method, which first selects an initial set of basis spectra from the original tone by some simple selection strategy, and then modifies and improves them. Matching results for a range of instruments show that local search, when using the best composite results found by various simple selection strategies, is as effective as the GA. Moreover, surprisingly, starting from various initial basis spectra, local search can usually find several different matches, all with errors within 1% of the best solution, indicating that there are many near-optimal solutions in the wavetable search space.

@article{wun2005a,
author={wun, simon and horner, andrew},
journal={journal of the audio engineering society},
title={a comparison between local search and genetic algorithm methods for wavetable matching},
year={2005},
volume={53},
number={4},
pages={314-325},
doi={},
month={april},} @article{wun2005a,
author={wun, simon and horner, andrew},
journal={journal of the audio engineering society},
title={a comparison between local search and genetic algorithm methods for wavetable matching},
year={2005},
volume={53},
number={4},
pages={314-325},
doi={},
month={april},
abstract={picking spectral snapshots from an original tone is a successful approach to finding basis spectra in wavetable matching of musical instrument tones. spectral snapshot selection usually requires optimization by a sophisticated technique such as a genetic algorithm (ga). ga selection is compared to a straightforward local search method, which first selects an initial set of basis spectra from the original tone by some simple selection strategy, and then modifies and improves them. matching results for a range of instruments show that local search, when using the best composite results found by various simple selection strategies, is as effective as the ga. moreover, surprisingly, starting from various initial basis spectra, local search can usually find several different matches, all with errors within 1% of the best solution, indicating that there are many near-optimal solutions in the wavetable search space.},}

TY - paper
TI - A Comparison between Local Search and Genetic Algorithm Methods for Wavetable Matching
SP - 314 EP - 325
AU - Wun, Simon
AU - Horner, Andrew
PY - 2005
JO - Journal of the Audio Engineering Society
IS - 4
VO - 53
VL - 53
Y1 - April 2005 TY - paper
TI - A Comparison between Local Search and Genetic Algorithm Methods for Wavetable Matching
SP - 314 EP - 325
AU - Wun, Simon
AU - Horner, Andrew
PY - 2005
JO - Journal of the Audio Engineering Society
IS - 4
VO - 53
VL - 53
Y1 - April 2005
AB - Picking spectral snapshots from an original tone is a successful approach to finding basis spectra in wavetable matching of musical instrument tones. Spectral snapshot selection usually requires optimization by a sophisticated technique such as a genetic algorithm (GA). GA selection is compared to a straightforward local search method, which first selects an initial set of basis spectra from the original tone by some simple selection strategy, and then modifies and improves them. Matching results for a range of instruments show that local search, when using the best composite results found by various simple selection strategies, is as effective as the GA. Moreover, surprisingly, starting from various initial basis spectra, local search can usually find several different matches, all with errors within 1% of the best solution, indicating that there are many near-optimal solutions in the wavetable search space.