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 (MFCC) were used as short time features. The
accuracy of the best performing model on this data set was 44% as compared to a human performance of 52% on the same data set.
Original language | English |
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Title of host publication | International Conference on Music Information Retrieval |
Publication date | 2005 |
Pages | 604-609 |
Publication status | Published - 2005 |
Event | 6th International Conference on Music Information Retrieval - London, United Kingdom Duration: 11 Sept 2005 → 15 Sept 2005 Conference number: 6 http://ismir2005.ismir.net/ |
Conference
Conference | 6th International Conference on Music Information Retrieval |
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Number | 6 |
Country/Territory | United Kingdom |
City | London |
Period | 11/09/2005 → 15/09/2005 |
Internet address |
Keywords
- Support Vector Machine
- Product Probability Kernel
- Convolution Kernel
- Music Genre
- Feature Integration