Computational strategies for breakbeat classification and resequencing in hardcore, jungle and drum and bass

dc.contributor.author Hockman,JA en
dc.contributor.author Matthew Davies en
dc.date.accessioned 2017-12-20T10:05:03Z
dc.date.available 2017-12-20T10:05:03Z
dc.date.issued 2015 en
dc.description.abstract The dance music genres of hardcore, jungle and drum and bass (HJDB) emerged in the United Kingdom during the early 1990s as a result of affordable consumer sampling technology and the popularity of rave music and culture. A key attribute of these genres is their usage of fast-paced drums known as breakbeats. Automated analysis of breakbeat usage in HJDB would allow for novel digital audio effects and musicological investigation of the genres. An obstacle in this regard is the automated identification of breakbeats used in HJDB music. This paper compares three strategies for breakbeat detection: (1) a generalised frame-based music classification scheme; (2) a specialised system that segments drums from the audio signal and labels them with an SVM classifier; (3) an alternative specialised approach using a deep network classifier. The results of our evaluations demonstrate the superiority of the specialised approaches, and highlight the need for style-specific workflows in the determination of particular musical attributes in idiosyncratic genres. We then leverage the output of the breakbeat classification system to produce an automated breakbeat sequence reconstruction, ultimately recreating the HJDB percussion arrangement. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/4381
dc.language eng en
dc.relation 5496 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Computational strategies for breakbeat classification and resequencing in hardcore, jungle and drum and bass en
dc.type conferenceObject en
dc.type Publication en
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