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

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.