Abstract

Beat tracking is what people do when they tap their feet in time to music. We present a software system which performs this task, processing music in a standard digital audio format and estimating the locations of musical beats. A time-domain algorithm detects salient acoustic events, and then a clustering algorithm groups the time intervals between events to obtain hypotheses about the current tempo. Multiple competing agents track these hypotheses throughout the music, with further agents being created at decision points. The output for each agent is a sequence of beat locations, which is evaluated for its closeness of t to the data. This approach to beat tracking assumes no previous knowledge of the music such as the style, time signature or approximate tempo; all required information is derived from the audio data. The system has been tested with various styles of music (popular, jazz, and classical) and performs robustly, rarely making errors in popular music, a...

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