Improving Music Genre Classification by Short-Time Feature Integration

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

Standard

Improving Music Genre Classification by Short-Time Feature Integration. / Meng, Anders; Ahrendt, Peter; Larsen, Jan.

IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005. p. 497-500.

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

Harvard

Meng, A, Ahrendt, P & Larsen, J 2005, 'Improving Music Genre Classification by Short-Time Feature Integration'. in IEEE International Conference on Acoustics, Speech, and Signal Processing. pp. 497-500., 10.1109/ICASSP.2005.1416349

APA

Meng, A., Ahrendt, P., & Larsen, J. (2005). Improving Music Genre Classification by Short-Time Feature Integration. In IEEE International Conference on Acoustics, Speech, and Signal Processing. (pp. 497-500). 10.1109/ICASSP.2005.1416349

CBE

Meng A, Ahrendt P, Larsen J. 2005. Improving Music Genre Classification by Short-Time Feature Integration. In IEEE International Conference on Acoustics, Speech, and Signal Processing. pp. 497-500. Available from: 10.1109/ICASSP.2005.1416349

MLA

Meng, Anders, Peter Ahrendt, and Jan Larsen "Improving Music Genre Classification by Short-Time Feature Integration". IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005. 497-500. Available: 10.1109/ICASSP.2005.1416349

Vancouver

Meng A, Ahrendt P, Larsen J. Improving Music Genre Classification by Short-Time Feature Integration. In IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005. p. 497-500. Available from: 10.1109/ICASSP.2005.1416349

Author

Meng, Anders; Ahrendt, Peter; Larsen, Jan / Improving Music Genre Classification by Short-Time Feature Integration.

IEEE International Conference on Acoustics, Speech, and Signal Processing. 2005. p. 497-500.

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

Bibtex

@inbook{711f9ac4484545b3a8008f715f46f546,
title = "Improving Music Genre Classification by Short-Time Feature Integration",
keywords = "Audio classification, early/late Information fusion,, Feature Integration",
author = "Anders Meng and Peter Ahrendt and Jan Larsen",
year = "2005",
doi = "10.1109/ICASSP.2005.1416349",
isbn = "0-7803-8874-7",
pages = "497-500",
booktitle = "IEEE International Conference on Acoustics, Speech, and Signal Processing",

}

RIS

TY - GEN

T1 - Improving Music Genre Classification by Short-Time Feature Integration

A1 - Meng,Anders

A1 - Ahrendt,Peter

A1 - Larsen,Jan

AU - Meng,Anders

AU - Ahrendt,Peter

AU - Larsen,Jan

PY - 2005

Y1 - 2005

N2 - Many different short-time features, using time windows in the size of 10-30 ms, have been proposed for music segmentation, retrieval and genre classification. However, often the available time frame of the music to make the actual decision or comparison (the decision time horizon) is in the range of seconds instead of milliseconds. The problem of making new features on the larger time scale from the short-time features (feature integration) has only received little attention. This paper investigates different methods for feature integration and late information fusion for music genre classification. A new feature integration technique, the AR model, is proposed and seemingly outperforms the commonly used mean-variance features.

AB - Many different short-time features, using time windows in the size of 10-30 ms, have been proposed for music segmentation, retrieval and genre classification. However, often the available time frame of the music to make the actual decision or comparison (the decision time horizon) is in the range of seconds instead of milliseconds. The problem of making new features on the larger time scale from the short-time features (feature integration) has only received little attention. This paper investigates different methods for feature integration and late information fusion for music genre classification. A new feature integration technique, the AR model, is proposed and seemingly outperforms the commonly used mean-variance features.

KW - Audio classification

KW - early/late Information fusion,

KW - Feature Integration

U2 - 10.1109/ICASSP.2005.1416349

DO - 10.1109/ICASSP.2005.1416349

SN - 0-7803-8874-7

BT - IEEE International Conference on Acoustics, Speech, and Signal Processing

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

SP - 497

EP - 500

ER -