PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Decision time horizon for music genre classification using short time features
Peter Ahrendt, Anders Meng and Jan Larsen
In: EUSIPCO 2004, 6-10 Sep 2004, Vienna.

Abstract

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 (This term refers to the decision making, i.e., early information fusion is an operation on the features before classification (and decision making). This is opposed to late information fusion (decision fusion) that assembles the information on the basis of the decisions) 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:154
Deposited By:Peter Ahrendt
Deposited On:01 June 2004