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The importance of repetitions in music is well-known. In this paper, we studymusic repetitions in the context of effective and efficient automatic genreclassification in large-scale music-databases. We aim at enhancing the accessand organization of pieces of music in Digital Libraries by allowing automaticcategorization of entire collections by considering only their musical content.We handover to the public a set of genre-specific patterns to support researchin musicology. The patterns can be used, for instance, to explore and analyzethe relations between musical genres. There are many existing algorithms thatcould be used to identify and extract repeating patterns in symbolicallyencoded music. In our case, the extracted patterns are used as representationsof the pieces of music on the underlying corpus and, consecutively, to trainand evaluate a classifier to automatically identify genres. In this paper, weapply two very fast algorithms enabling us to experiment on large and diversecorpora. Thus, we are able to find patterns with strong discrimination powerthat can be used in various applications. We carried out experiments on acorpus containing over 40,000 MIDI files annotated with at least one genre. Theexperiments suggest that our approach is scalable and capable of dealing withreal-world-size music collections.

On large-scale genre classification in symbolically encoded music by automatic identification of repeating patterns