| Paper: | MLSP-P4.5 | ||
| Session: | Audio and Communication Applications | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Speech and Audio Processing Applications | ||
| Title: | A Markov-Chain Monte-Carlo Approach to Musical Audio Segmentation | ||
| Authors: | Christophe Rhodes, Michael Casey, University of London, United Kingdom; Samer Abdallah, Mark Sandler, Queen Mary, University of London, United Kingdom | ||
| Abstract: | This paper describes a method for automatically segmenting and labelling sections in recordings of musical audio. We incorporate the user's expectations for segment duration as an explicit prior probability distribution in a Bayesian framework, and demonstrate experimentally that this method can produce accurate labelled segmentations for popular music. | ||
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