Co-occurrence Models in Music Genre Classification

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2005

Standard

Co-occurrence Models in Music Genre Classification. / Ahrendt, Peter; Goutte, Cyril; Larsen, Jan.

IEEE International workshop on Machine Learning for Signal Processing. IEEE, 2005. p. 247-252.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2005

Harvard

Ahrendt, P, Goutte, C & Larsen, J 2005, 'Co-occurrence Models in Music Genre Classification'. in IEEE International workshop on Machine Learning for Signal Processing. IEEE, pp. 247-252., 10.1109/MLSP.2005.1532908

APA

Ahrendt, P., Goutte, C., & Larsen, J. (2005). Co-occurrence Models in Music Genre Classification. In IEEE International workshop on Machine Learning for Signal Processing. (pp. 247-252). IEEE. 10.1109/MLSP.2005.1532908

CBE

Ahrendt P, Goutte C, Larsen J. 2005. Co-occurrence Models in Music Genre Classification. In IEEE International workshop on Machine Learning for Signal Processing. IEEE. pp. 247-252. Available from: 10.1109/MLSP.2005.1532908

MLA

Ahrendt, Peter, Cyril Goutte, and Jan Larsen "Co-occurrence Models in Music Genre Classification". IEEE International workshop on Machine Learning for Signal Processing. IEEE. 2005. 247-252. Available: 10.1109/MLSP.2005.1532908

Vancouver

Ahrendt P, Goutte C, Larsen J. Co-occurrence Models in Music Genre Classification. In IEEE International workshop on Machine Learning for Signal Processing. IEEE. 2005. p. 247-252. Available from: 10.1109/MLSP.2005.1532908

Author

Ahrendt, Peter; Goutte, Cyril; Larsen, Jan / Co-occurrence Models in Music Genre Classification.

IEEE International workshop on Machine Learning for Signal Processing. IEEE, 2005. p. 247-252.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2005

Bibtex

@inbook{45fceb023c5b4a0c9b639dc9e95f4780,
title = "Co-occurrence Models in Music Genre Classification",
publisher = "IEEE",
author = "Peter Ahrendt and Cyril Goutte and Jan Larsen",
year = "2005",
doi = "10.1109/MLSP.2005.1532908",
isbn = "0-7803-9517-4",
pages = "247-252",
booktitle = "IEEE International workshop on Machine Learning for Signal Processing",

}

RIS

TY - GEN

T1 - Co-occurrence Models in Music Genre Classification

A1 - Ahrendt,Peter

A1 - Goutte,Cyril

A1 - Larsen,Jan

AU - Ahrendt,Peter

AU - Goutte,Cyril

AU - Larsen,Jan

PB - IEEE

PY - 2005

Y1 - 2005

N2 - Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors x\_r which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider the whole song as an integrated part of the probabilistic model. This was achieved by considering a song as a set of independent co-occurrences (s, x\_r) (s is the song index) instead of just a set of independent (x\_r)'s. The models were tested against two baseline classification methods on a difficult 11 genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models.

AB - Music genre classification has been investigated using many different methods, but most of them build on probabilistic models of feature vectors x\_r which only represent the short time segment with index r of the song. Here, three different co-occurrence models are proposed which instead consider the whole song as an integrated part of the probabilistic model. This was achieved by considering a song as a set of independent co-occurrences (s, x\_r) (s is the song index) instead of just a set of independent (x\_r)'s. The models were tested against two baseline classification methods on a difficult 11 genre data set with a variety of modern music. The basis was a so-called AR feature representation of the music. Besides the benefit of having proper probabilistic models of the whole song, the lowest classification test errors were found using one of the proposed models.

KW - probabilistic models

KW - music genre classification

U2 - 10.1109/MLSP.2005.1532908

DO - 10.1109/MLSP.2005.1532908

SN - 0-7803-9517-4

BT - IEEE International workshop on Machine Learning for Signal Processing

T2 - IEEE International workshop on Machine Learning for Signal Processing

SP - 247

EP - 252

ER -