Bits and Beats: Computing Rhythmic Information as Bitwise Operations Optimized for Machine Learning

Effective Machine Learning algorithms require meticulous data preparation. In contemporary microprocessors, ensuring predictable and efficient memory access is crucial to mitigate cache misses. Therefore, compact data structures with high information density are crucial for optimal computation. This paper discusses the interplay of musical meter and microtiming, as long-term expectation of pattern repetition and short-term deviations. A review of musicological, cognitive and computational models is presented. The literature regards meter perception of rhythmic patterns as a manifestation of human attentive behavior, which is also culturally dependent. Additionally, the paper discusses dissimilarity measures of rhythmic patters. The author introduces two algorithms featuring normalized binary representations of rhythmic patterns of inter-onset intervals, activation functions for computing linear, and logarithmic time differences. Bitwise operations are implemented for the bit-sequences of rhythmic patterns. The author offers an implementation of mutual information calculation as bitwise operations.