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A novel method is introduced for automatic mix recreation using timbral classification techniques and an optimization algorithm. This approach uses the Euclidean distance between modified Spectral Histograms to calculate the distance between a mix and a target sound and uses a genetic optimization algorithm to figure out the best coefficients for that mix. The implementation has been shown to successfully recreate multitrack mixes accurately and may pave the way towards the automatic mixing of novel multitrack sessions based on a desired target sound.
Author (s): Kolasinski, Bennett;
Affiliation:
New York University
(See document for exact affiliation information.)
AES Convention: 124
Paper Number:7496
Publication Date:
2008-05-06
Session subject:
Software, Instrumentation, and Measurement
DOI:
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Kolasinski, Bennett; 2008; A Framework for Automatic Mixing Using Timbral Similarity Measures and Genetic Optimization [PDF]; New York University; Paper 7496; Available from: https://aes.org/publications/elibrary-page/?id=14626
Kolasinski, Bennett; A Framework for Automatic Mixing Using Timbral Similarity Measures and Genetic Optimization [PDF]; New York University; Paper 7496; 2008 Available: https://aes.org/publications/elibrary-page/?id=14626
@inproceedings{Kolasinski2008a,
title={{A Framework for Automatic Mixing Using Timbral Similarity Measures and Genetic Optimization}},
author={Kolasinski, Bennett},
year={2008},
month={may},
booktitle={Journal of the Audio Engineering Society},
publisher={Paper 7496; AES Convention 124; May 2008},
number={7496},
organization={AES},
}
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