Improving Music Genre Classification by Short-Time Feature Integration | Anders Meng, Peter Ahrendt, Jan Larsen
| | Abstract | 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. | | Keywords | Audio classification, Feature Integration, early/late Information fusion, | | Type | Conference paper [With referee] | | Conference | IEEE International Conference on Acoustics, Speech, and Signal Processing | | Year | 2005 Month March Vol. V pp. 497-500 | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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