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Beat Tracking with Musical Knowledge

Simon Dixon, Emilios Cambouropoulos

When a person taps a foot in time with a piece of music, they are performing beat tracking. Beat tracking is fundamental to the understanding of musical structure, and therefore an essential ability for any system which purports to exhibit musical intelligence or understanding. We present a multiple agent beat tracking system which performs this task, estimating the locations of musical beats in musical performance data. This approach to beat tracking requires no prior information about the input data, such as the tempo or time signature; all required information is derived from the performance data. For constant tempo performances, previous beat tracking systems have proved successful; however, these systems fail when there are large variations in tempo. We examine the role of musical knowledge in guiding the beat tracking process, and show that a system equipped with knowledge of musical salience is able to track the beat of music even in the presence of large tempo variations. Results are presented for a large corpus of expressively performed classical piano music (13 complete sonatas), containing a full range of tempos and much variability in tempo within sections. With the musical knowledge disabled, the beats are tracked about 75% correctly; the inclusion of musical knowledge raises this figure to over 90%.

Keywords: Art and Music, Multi-Agent Systems

Citation: Simon Dixon, Emilios Cambouropoulos: Beat Tracking with Musical Knowledge. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.626-630.

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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.