Improving Music Genre Classification by Short Time Feature Integration

Publication: ResearchPoster – Annual report year: 2005

Standard

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

2005. Poster session presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States.

Publication: ResearchPoster – Annual report year: 2005

Harvard

Meng, A, Ahrendt, P & Larsen, J 2005, 'Improving Music Genre Classification by Short Time Feature Integration' IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States, 23/03/05,

APA

Meng, A., Ahrendt, P., & Larsen, J. (2005). Improving Music Genre Classification by Short Time Feature Integration. Poster session presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States.

CBE

Meng A, Ahrendt P, Larsen J. 2005. Improving Music Genre Classification by Short Time Feature Integration. Poster session presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States.

MLA

Vancouver

Meng A, Ahrendt P, Larsen J. Improving Music Genre Classification by Short Time Feature Integration. 2005. Poster session presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States.

Author

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

2005. Poster session presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, Pennsylvania, United States.

Publication: ResearchPoster – Annual report year: 2005

Bibtex

@misc{551fef7845e443bfbae4b2677b1f10fd,
title = "Improving Music Genre Classification by Short Time Feature Integration",
keywords = "Information Fusion, Music genre, Autoregressive Model",
author = "Anders Meng and Peter Ahrendt and Jan Larsen",
year = "2005",
type = "ConferencePaper <importModel: ConferenceImportModel>",

}

RIS

TY - CONF

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 (derived from 10-30ms of audio) have been proposed for music segmentation, retrieval and genre classification. Often the available time frame of the music to make a decision (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 (early information fusion) and late information fusion (assembling of probabilistic outputs or decisions from the classifier, e.g. majority voting) for music genre classification.

AB - Many different short-time features (derived from 10-30ms of audio) have been proposed for music segmentation, retrieval and genre classification. Often the available time frame of the music to make a decision (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 (early information fusion) and late information fusion (assembling of probabilistic outputs or decisions from the classifier, e.g. majority voting) for music genre classification.

KW - Information Fusion

KW - Music genre

KW - Autoregressive Model

ER -