A lightweight multi-agent musical beat tracking system

Authors:
Simon Dixon
Affiliations:
Austrian Research Institute for Artificial Intelligence, Schottengcisse, Vienna, Austria
Venue:
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Year:
2000

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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 fit 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, and recovering quickly from errors in more complex styles of music, despite the fact that no high level musical knowledge is encoded in the system.