Computational Approach to Track Beats in Improvisational Music Performance

Beat tracking, or identifying the temporal locations of beats in a musical recording, has a variety of applications that range from music information retrieval to machine listening. Algorithms designed to monitor the tempo of a musical recording have thus far been optimized for music with relatively stable rhythms, repetitive structures, and consistent melodies; these algorithms typically struggle to follow the free-form nature of improvisational music. Here, we present a multi-agent improvisation beat tracker (MAIBT) that addresses the challenges posed by improvisations and compare its performance with other state-of-the-art methods on a unique data set collected during improvisational music therapy sessions. This algorithm is designed for MIDI files and proceeds in four stages: (1) preprocessing to remove notes that are timid and overlapping, (2) clustering of the remaining notes and subsequent ranking of the clusters, (3) agent initialization and performance-based selection, and (4) artificial beat insertion and deletion to fill remaining beat gaps and create a comprehensive beat sequence. This particular method performs better than other generic beat-tracking approaches for music that lacks regularity; it is thus well suited to applications where unpredictability and inaccuracy are predominant, such as in music therapy improvisation.