A Novel 1D State Space for Efficient Music Rhythmic Analysis

TL;DR

This paper introduces a 1D state space model for music rhythmic analysis that significantly reduces computational complexity while maintaining performance, enabling faster processing of large music collections.

Contribution

It proposes a novel 1D state space and semi-Markov model that simplifies traditional 2D models for efficient music time structure analysis.

Findings

Achieves over 30 times speedup compared to previous methods.

Maintains similar accuracy to state-of-the-art joint models.

Reduces computational costs for large-scale music analysis.

Abstract

Inferring music time structures has a broad range of applications in music production, processing and analysis. Scholars have proposed various methods to analyze different aspects of time structures, such as beat, downbeat, tempo and meter. Many state-of-the-art (SOFA) methods, however, are computationally expensive. This makes them inapplicable in real-world industrial settings where the scale of the music collections can be millions. This paper proposes a new state space and a semi-Markov model for music time structure analysis. The proposed approach turns the commonly used 2D state spaces into a 1D model through a jump-back reward strategy. It reduces the state spaces size drastically. We then utilize the proposed method for causal, joint beat, downbeat, tempo, and meter tracking, and compare it against several previous methods. The proposed method delivers similar performance with the SOFA joint causal models with a much smaller state space and a more than 30 times speedup.