Measuring the similarity of Rhythmic Patterns

A system is described which measures the similarity of two arbitrary rhythmic patterns. The patterns are represented as acoustic signals, and are not assumed to have been performed with similar sound sets. Two novel methods are presented that constitute the algorithmic core of the system. First, a probabilistic musical meter estimation process is described, which segments a continuous musical signal into patterns. As a side-product, the method outputs tatum, tactus (beat), and measure lengths. A subsequent process performs the actual similarity measurements. Acoustic features are extracted which model the fluctuation of loudness and brightness within the pattern, and dynamic time warping is then applied to align the patterns to be compared. In simulations, the system behaved consistently by assigning high similarity measures to similar musical rhythms, even when performed using different sound sets.