Feature analysis and classification of classical musical instruments: an empirical study

Authors:
Christian Simmermacher;Da Deng;Stephen Cranefield
Affiliations:
Department of Information Science, University of Otago, New Zealand;Department of Information Science, University of Otago, New Zealand;Department of Information Science, University of Otago, New Zealand
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

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Abstract

We present an empirical study on classical music instrument classification. A methodology with feature extraction and evaluation is proposed and assessed with a number of experiments, whose final stage is to detect instruments in solo passages. In feature selection it is found that similar but different rankings for individual tone classification and solo passage instrument recognition are reported. Based on the feature selection results, excerpts from concerto and sonata files are processed, so as to detect and distinguish four major instruments in solo passages: trumpet, flute, violin, and piano. Nineteen features selected from the Mel-frequency cepstral coefficients (MFCC) and the MPEG-7 audio descriptors achieve a recognition rate of around 94% by the best classifier assessed by cross validation.