On the analysis of musical expression in audio signals

In western art music, composers communicate their work to performers via a standard notation which specificies the musical pitches and relative timings of notes. This notation may also include some higher level information such as variations in the dynamics, tempo and timing. Famous performers are characterised by their expressive interpretation, the ability to convey structural and emotive information within the given framework. The majority of work on audio content analysis focusses on retrieving score-level information; this paper reports on the extraction of parameters describing the performance, a task which requires a much higher degree of accuracy. Two systems are presented: BeatRoot, an off-line beat tracking system which finds the times of musical beats and tracks changes in tempo throughout a performance, and the Performance Worm, a system which provides a real-time visualisation of the two most important expressive dimensions, tempo and dynamics. Both of these systems are being used to process data for a large-scale study of musical expression in classical and romantic piano performance, which uses artificial intelligence (machine learning) techniques to discover fundamental patterns or principles governing expressive performance.