Decision Time Horizon for Music Genre Classification using Short Time Features

Peter Ahrendt, Anders Meng, Jan Larsen

AbstractIn this paper music genre classification has been explored with special emphasis on the decision time horizon and ranking of tapped-delay-line short-time features. Late information fusion as e.g. majority voting is compared with techniques of early information fusion such as dynamic PCA (DPCA). The most frequently suggested features in the literature were employed including mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), zero-crossing rate (ZCR), and MPEG-7 features. To rank the importance of the short time features consensus sensitivity analysis is applied. A Gaussian classifier (GC) with full covariance structure and a linear neural network (NN) classifier are used.
Keywordsmusic genre classification, decision time horizon, feature ranking, dynamic PCA, majority voting
TypeConference paper [With referee]
Year2004    Month September    pp. 1293--1296
AddressVienna, Austria
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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