An investigation of feature models for music genre classification using the support vector classifier
An investigation of feature models for music genre classification using the support vector classifier
In music genre classification the decision time is typically of the order of several seconds, however, most automatic music genre classification systems focus on short time features derived from 10?50ms. This work investigates two models, the multivariate Gaussian model and the multivariate autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product probability kernel. In order to examine the different methods an 11 genre music setup was utilized. In this setup the Mel Frequency Cepstral Coefficients were used as short time features. The accuracy of the best performing model on this data set was 44% compared to a human performance of 52% on the same data set.
Feature Integration, Product Probability Kernel, Convolution Kernel, Support Vector Machine, Music Genre
0955117909
604-609
Shawe-Taylor, J.S.
ef88d96d-9421-4186-a7f9-6c5024984c78
Meng, A.
0f859d0e-5b58-4aa9-b29e-9744bc8dd17d
2005
Shawe-Taylor, J.S.
ef88d96d-9421-4186-a7f9-6c5024984c78
Meng, A.
0f859d0e-5b58-4aa9-b29e-9744bc8dd17d
Shawe-Taylor, J.S. and Meng, A.
(2005)
An investigation of feature models for music genre classification using the support vector classifier.
6th International Conference on Music Information Retrieval, ISMIR 2005, Queen Mary, University of London, London, United Kingdom.
11 - 15 Sep 2005.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
In music genre classification the decision time is typically of the order of several seconds, however, most automatic music genre classification systems focus on short time features derived from 10?50ms. This work investigates two models, the multivariate Gaussian model and the multivariate autoregressive model for modelling short time features. Furthermore, it was investigated how these models can be integrated over a segment of short time features into a kernel such that a support vector machine can be applied. Two kernels with this property were considered, the convolution kernel and product probability kernel. In order to examine the different methods an 11 genre music setup was utilized. In this setup the Mel Frequency Cepstral Coefficients were used as short time features. The accuracy of the best performing model on this data set was 44% compared to a human performance of 52% on the same data set.
Text
ismir 05 meng taylor short 24 Nov 05
- Other
More information
Published date: 2005
Venue - Dates:
6th International Conference on Music Information Retrieval, ISMIR 2005, Queen Mary, University of London, London, United Kingdom, 2005-09-11 - 2005-09-15
Keywords:
Feature Integration, Product Probability Kernel, Convolution Kernel, Support Vector Machine, Music Genre
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 261575
URI: http://eprints.soton.ac.uk/id/eprint/261575
ISBN: 0955117909
PURE UUID: abc01977-4b13-47c7-80c4-f64cd3c5ff0c
Catalogue record
Date deposited: 24 Nov 2005
Last modified: 14 Mar 2024 06:55
Export record
Contributors
Author:
J.S. Shawe-Taylor
Author:
A. Meng
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics