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

A Novel 1D State Space for Efficient Music Rhythmic Analysis