Music Genre Classification using the multivariate AR feature integration model

Publication: Research - peer-reviewReport – Annual report year: 2005

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Music Genre Classification using the multivariate AR feature integration model. / Ahrendt, Peter; Meng, Anders.

2005.

Publication: Research - peer-reviewReport – Annual report year: 2005

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Ahrendt, Peter; Meng, Anders / Music Genre Classification using the multivariate AR feature integration model.

2005.

Publication: Research - peer-reviewReport – Annual report year: 2005

Bibtex

@book{1b17e3c7b41d45068e27f10ba29b103c,
title = "Music Genre Classification using the multivariate AR feature integration model",
keywords = "Multivariate Autoregressive Model, Music Genre Classification",
author = "Peter Ahrendt and Anders Meng",
year = "2005",

}

RIS

TY - RPRT

T1 - Music Genre Classification using the multivariate AR feature integration model

AU - Ahrendt,Peter

AU - Meng,Anders

PY - 2005

Y1 - 2005

N2 - Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10-30ms, although several characteristics of music require larger time scales. Thus, larger time frames are needed to take informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate autoregressive model. Other authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005).

AB - Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10-30ms, although several characteristics of music require larger time scales. Thus, larger time frames are needed to take informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate autoregressive model. Other authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005).

KW - Multivariate Autoregressive Model

KW - Music Genre Classification

M3 - Report

BT - Music Genre Classification using the multivariate AR feature integration model

ER -