Decision time horizon for music genre classification using short time features

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2004

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Decision time horizon for music genre classification using short time features. / Ahrendt, Peter; Meng, Anders; Larsen, Jan.

EUSIPCO. 2004. p. 1293-1296.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2004

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Author

Ahrendt, Peter; Meng, Anders; Larsen, Jan / Decision time horizon for music genre classification using short time features.

EUSIPCO. 2004. p. 1293-1296.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2004

Bibtex

@inbook{63b4a84ba9ec4b7f85985cd6caad60ad,
title = "Decision time horizon for music genre classification using short time features",
author = "Peter Ahrendt and Anders Meng and Jan Larsen",
year = "2004",
pages = "1293-1296",
booktitle = "EUSIPCO",

}

RIS

TY - GEN

T1 - Decision time horizon for music genre classification using short time features

A1 - Ahrendt,Peter

A1 - Meng,Anders

A1 - Larsen,Jan

AU - Ahrendt,Peter

AU - Meng,Anders

AU - Larsen,Jan

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - decision time horizon

KW - feature ranking

KW - music genre classification

KW - majority voting

KW - dynamic PCA

UR - http://www.imm.dtu.dk/pubdb/p.php?2981

BT - EUSIPCO

T2 - EUSIPCO

SP - 1293

EP - 1296

ER -