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Peter Ahrendt

Decision time horizon for music genre classification using short time features

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

In 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.
Original languageEnglish
Title of host publicationProceedings of European Signal Processing Conference (EUSIPCO), Vienna, Austria, September 2004.
Number of pages4
Publication year2004
Publication statusPublished - 2004
Externally publishedYes

    Research areas

  • decision time horizon, feature ranking, music genre classification, majority voting, dynamic PCA

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