Using duration models to reduce fragmentation in audio segmentation

  • Authors:

  • Samer Abdallah;Mark Sandler;Christophe Rhodes;Michael Casey

  • Affiliations:

  • Queen Mary, University of London, London E1 4NS;Queen Mary, University of London, London E1 4NS;Goldsmiths College, University of London, London SE14 6NW;Goldsmiths College, University of London, London SE14 6NW

  • Venue:
  • Machine Learning
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.