Time-scale transformations of audio signals have traditionally relied exclusively upon manipulations of tempo. We present a novel technique for automatic mixing and synchronization between two musical signals. In this transformation, the original signal assumes the tempo, meter, and rhythmic structure of the model signal, while the extracted downbeats and salient intra-measure infrastructure of the original are maintained.

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The dance music genres of hardcore, jungle and drum & 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.

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