Relational motif discovery via graph spectral ranking

  • Authors:
  • Alberto Pinto

  • Affiliations:
  • Università degli Studi di Milano, Milano, Italy

  • Venue:
  • Proceedings of the Eighth Workshop on Mining and Learning with Graphs
  • Year:
  • 2010

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Abstract

Music summarization aims at finding the most representative parts of a music piece (motifs) that can be exploited for efficient music indexing. Here we present a novel approach for motif discovery in music pieces based on an graph spectral ranking. Scores are segmented into a network graph of music segments and then ranked depending on their centrality. Different poli- and mono-phonic metric concepts can be adopted to compare music segments. Bars with higher centrality are more relevant for music summarization. We present an evaluation on the corpus of J. S. Bach's 2-part Inventions both in poli- and mono-phonic configuration.