A wavelet-based approach to the discovery of themes and sections in monophonic melodies

Gissel Velarde, David Meredith

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We present the computational method submitted to the MIREX 2014 Discovery of Repeated Themes & Sections task, and the results on the monophonic version of the JKU Patterns Development Database. In the context of pattern discovery in monophonic music, the idea behind our method is that, with a good melodic structure in terms of segments, it should be possible to gather similar segments
into clusters and rank their salience within the piece. We present an approach to this problem and how we address it. In general terms, we represent melodies
either as raw 1D pitch signals or as these signals filtered with the continuous wavelet transform (CWT) using the Haar wavelet. We then segment the signal either into constant duration segments or at the resulting coefficients’ modulus local maxima. Segments are concatenated based on their contiguous city-block distance. The concatenated segments are compared using city-block distance and clustered using an agglomerative hierarchical cluster tree. Finally, clusters are ranked according the sum of the length of segments’ occurrences. We present
the results of our method on the JKU Patterns Development Database.
Original languageEnglish
Publication date22 Oct 2014
Number of pages4
Publication statusPublished - 22 Oct 2014
EventInternational Symposium on Music Information Retrieval: ISMIR - Taipei, Taiwan, Province of China
Duration: 27 Oct 201431 Oct 2014


ConferenceInternational Symposium on Music Information Retrieval
Country/TerritoryTaiwan, Province of China


  • Pattern discovery
  • continous wavelet transform
  • Haar wavelet
  • monophonic melodies
  • clustering


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