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A NOVEL 1D STATE SPACE FOR EFFICIENT MUSIC RHYTHMIC ANALYSIS.pdf (291.35 kB)
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A NOVEL 1D STATE SPACE FOR EFFICIENT MUSIC RHYTHMIC ANALYSIS

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posted on 23.02.2022, 03:46 by Mojtaba HeydariMojtaba Heydari, Mathew McCallum, Andreas Ehmann, Zhiyao Duan
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.

Funding

CAREER: Human-Computer Collaborative Music Making

Directorate for Computer & Information Science & Engineering

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Email Address of Submitting Author

mheydari@ur.rochester.edu

Submitting Author's Institution

University of Rochester

Submitting Author's Country

United States of America