Unsupervised Detection of Music Boundaries by Time Series Structure Features


Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogene-ity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.

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Machine learning in time series databases (and everything is a time series!)

  • E Keogh
  • 2011
1 Excerpt

MIREX 2010 music structure segmentation task: IRCAMSUMMARY submission. Music Information Retrieval Evaluation eXchange (MIREX)

  • G Peeters
  • 2010
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