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

IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY

13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio

Timbre-invariant Audio Features for Style Analysis of Classical Music

Copyright: (c) 2014 Christof Weiß et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Performance Following: Real-Time Prediction of Musical Sequences Without a Score

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