Improving Genre Classification by Combination of Audio and Symbolic Descriptors Using a Transcription System

T. Lidy, A. Rauber, A. Pertusa, J. Iñesta:
"Improving Genre Classification by Combination of Audio and Symbolic Descriptors Using a Transcription System";
Vortrag: International Conference on Music Information Retrieval (ISMIR), Vienna, Austria; 23.09.2007 - 27.09.2007; in:"Proceedings of the 8th International Conference on Music Information Retrieval", S. Dixon, D. Bainbridge, R. Typke (Hrg.);Österreichische Computer Gesellschaft, (2007), ISBN: 978-3-85403-218-2; S. 61 - 66.

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Abstract:


Recent research in music genre classification hints at a
glass ceiling being reached using timbral audio features.
To overcome this, the combination of multiple different
feature sets bearing diverse characteristics is needed. We
propose a new approach to extend the scope of the features:
We transcribe audio data into a symbolic form using
a transcription system, extract symbolic descriptors from
that representation and combine them with audio features.
With this method, we are able to surpass the glass ceiling
and to further improve music genre classification, as
shown in the experiments through three reference music
databases and comparison to previously published performance
results.