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Instrument recognition in polyphonic music based on automatic taxonomies (2006)
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| Venue: | IEEE Transactions on Speech and Audio Processing |
| Citations: | 63 - 9 self |
BibTeX
@INPROCEEDINGS{Essid06instrumentrecognition,
author = {Slim Essid and Gaël Richard},
title = {Instrument recognition in polyphonic music based on automatic taxonomies},
booktitle = {IEEE Transactions on Speech and Audio Processing},
year = {2006},
pages = {68--80}
}
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Abstract
We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, Signal to Mask Ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e. jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53%.
Keyphrases
polyphonic music automatic taxonomy instrument recognition varied database wide set possible combination sound excerpt prior musical source separation real music orchestration singing voice average accuracy hierarchical clustering algorithm useful feature robust probabilistic distance mask ratio new proposal musical ensemble new approach commercial recording acoustic feature audio classification single music genre
