Classifying Music Audio with Timbral and Chroma Features

Ellis, Daniel P. W.

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.


Also Published In

ISMIR 2007: Proceedings of the 8th International Conference on Music Information Retrieval: September 23-27, 2007, Vienna, Austria
Austrian Computer Society

More About This Work

Academic Units
Electrical Engineering
Published Here
June 27, 2012