Dynamic Time Signature Recognition, Tempo Inference, and Beat Tracking Through the Metrogram Transform

Abstract

This paper proposes a probabilistic approach for extracting time-varying and irregular time signature information from polyphonic audio extracts, subsequently providing beat and bar line positions given inferred time signature divisions. This is achieved via dynamically evaluating the beat tempo as a function of time through finding an optimal compromise in beat and bar alignment in the time and tempo domains. Time signature divisions are determined based on a new representation, termed the Metrogram, that presents time-varying information regarding rhythmic and metric periodicities in the Tempogram. Our methodology is characterised by its ability to provide a distribution over metric interpretations, offering insights into the diverse ways music can be rhythmically perceived. Results indicate high-level accuracy for a variety of polyphonic extracts containing irregular, complex, irrational, and time-varying time signatures. Accuracy rivalling state-of-the-art methodologies is also reported in a beat tracking task performed on the standard Ballroom Dataset. The paper offers insights into the field of dynamic time signature recognition and beat tracking, offering a valuable and versatile resource for the analysis, composition, and performance of music.