Eigenvector-based Relational Motif Discovery

The development of novel analytical tools to investigate the structure of music works is central in current music information retrieval research. In particular, music summarization aims at finding the most representative parts of a music piece (motifs) that can be exploited for an efficient music database indexing system. Here we present a novel approach for motif discovery in music pieces based on an eigenvector method. Scores are segmented into a network of bars and then ranked depending on their centrality. Bars with higher centrality are more likely to be relevant for music summarization. Results on the corpus of J.S.Bach’s 2-part Inventions demonstrate the effectiveness of the method and suggest that different musical metrics might be more suitable than others for different applications.