Finding an Optimal Segmentation for Audio Genre Classification

West, K. and Cox, S. J. (2005) Finding an Optimal Segmentation for Audio Genre Classification. In: Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR 2005), 2005-09-11 - 2005-09-15.

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Abstract

In the automatic classification of music many different segmentations of the audio signal have been used to calculate features. These include individual short frames (23 ms), longer frames (200 ms), short sliding textural windows (1 sec) of a stream of 23 ms frames, large fixed windows (10 sec) and whole files. In this work we present an evaluation of these different segmentations, showing that they are sub-optimal for genre classification and introduce the use of an onset detection based segmentation, which appears to outperform all of the fixed and sliding windows segmentation schemes in terms of classification accuracy and model size.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 20 Jul 2011 15:48
Last Modified: 22 Apr 2020 09:09
URI: https://ueaeprints.uea.ac.uk/id/eprint/22834
DOI:

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