Using Duration Models To Reduce Fragmentation in Audio Segmentation

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Abdallah, Samer; Sandler, Mark; Rhodes, Christophe and Casey, Michael A.. 2006. Using Duration Models To Reduce Fragmentation in Audio Segmentation. Machine Learning, 65(2-3), pp. 485-515. ISSN 0885-6125 [Article]

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Official URL: http://portal.acm.org/citation.cfm?id=1188034

Abstract or Description

We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Markov Chain Monte Carlo methods; in particular, we introduce a modification to Wolff's algorithm to make it applicable to a segment classification model with an arbitrary duration prior. We apply this to a collection of pop songs, and show experimentally that the generated segmentations suffer much less from fragmentation than those produced by segmentation algorithms based on clustering, and are closer to an expert listener's annotations, as evaluated by two different performance measures.

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Article

Identification Number (DOI):

https://doi.org/10.1007/s10994-006-0586-4