A Novel 1D State Space for Efficient Music Rhythmic Analysis
This addresses the need for efficient music rhythmic analysis in industrial settings with large-scale music collections, though it is incremental as it builds on existing joint causal models.
The paper tackled the problem of computationally expensive music time structure analysis by proposing a new 1D state space and semi-Markov model, achieving similar performance to state-of-the-art methods with a more than 30 times speedup.
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.