2007 Articles
Classifying Music Audio with Timbral and Chroma Features
Music audio classification has most often been addressed by modeling the statistics of broad spectral features, which, by design, exclude pitch information and reflect mainly instrumentation. We investigate using instead beat-synchronous chroma features, designed to reflect melodic and harmonic content and be invariant to instrumentation. Chroma features are less informative for classes such as artist, but contain information that is almost entirely independent of the spectral features, and hence the two can be profitably combined: Using a simple Gaussian classifier on a 20-way pop music artist identification task, we achieve 54% accuracy with MFCCs, 30% with chroma vectors, and 57% by combining the two. All the data and Matlab code to obtain these results are available.
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Files
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Ellis07-timbrechroma.pdf application/pdf 69 KB Download File
Also Published In
- Title
- ISMIR 2007: Proceedings of the 8th International Conference on Music Information Retrieval: September 23-27, 2007, Vienna, Austria
- Publisher
- Austrian Computer Society
More About This Work
- Academic Units
- Electrical Engineering
- Published Here
- June 27, 2012