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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

The importance of repetitions in music is well-known. In this paper, we study music repetitions in the context of effective and efficient automatic genre classification in large-scale music-databases. We aim at enhancing the access and organization of pieces of music in Digital Libraries by allowing automatic categorization of entire collections by considering only their musical content. We handover to the public a set of genre-specific patterns to support research in musicology. The patterns can be used, for instance, to explore and analyze the relations between musical genres.

There are many existing algorithms that could be used to identify and extract repeating patterns in symbolically encoded music. In our case, the extracted patterns are used as representations of the pieces of music on the underlying corpus and, consecutively, to train and evaluate a classifier to automatically identify genres. In this paper, we apply two very fast algorithms enabling us to experiment on large and diverse corpora. Thus, we are able to find patterns with strong discrimination power that can be used in various applications. We carried out experiments on a corpus containing over 40,000 MIDI files annotated with at least one genre. The experiments suggest that our approach is scalable and capable of dealing with real-world-size music collections.
Alkuperäiskielienglanti
OtsikkoDLfM '18 Proceedings of the 5th International Conference on Digital Libraries for Musicology
Sivumäärä4
JulkaisupaikkaNew York, NY
KustantajaACM
Julkaisupäivä28 syyskuuta 2018
Sivut34-37
ISBN (elektroninen)978-1-4503-6522-2
DOI - pysyväislinkit
TilaJulkaistu - 28 syyskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaDigital Libraries for Musicology 2018 - IRCAM, Paris, Ranska
Kesto: 28 syyskuuta 201828 syyskuuta 2018
https://dlfm.web.ox.ac.uk/workshops/dlfm-2018/programme

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet
  • 6131 Teatteri, tanssi, musiikki, muut esittävät taiteet

Siteeraa tätä

Ferraro, A., & Lemström, K. M. B. (2018). On large-scale genre classification in symbolically encoded music by automatic identification of repeating patterns. teoksessa DLfM '18 Proceedings of the 5th International Conference on Digital Libraries for Musicology (Sivut 34-37). ACM. https://doi.org/10.1145/3273024.3273035