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Improving Perceived Tempo Estimation by Statistical Modeling of Higher-Level Musical Descriptors

Conventional tempo estimation algorithms generally work by detecting significant audio events and finding periodicities of repetitive patterns in an audio signal. However, human perception of tempo is subjective, and relies on a far richer set of information, causing many tempo estimation algorithms to suffer from octave errors, or “double/half-time” confusion. In this paper, we propose a system that uses higher-level musical descriptors such as mood to train a statistical model of perceived tempo classes, which can then used to correct the estimate from a conventional tempo estimation algorithm. Our experimental results show reliable classification of perceived tempo class, as well as a significant reduction of octave errors when applied to an array of available tempo estimation algorithms.

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