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In a recent conference paper, we have reported a rhythm transcription methodbased on a merged-output hidden Markov model (HMM) that explicitly describesthe multiple-voice structure of polyphonic music. This model solves a majorproblem of conventional methods that could not properly describe the nature ofmultiple voices as in polyrhythmic scores or in the phenomenon of loosesynchrony between voices. In this paper we present a complete description ofthe proposed model and develop an inference technique, which is valid for anymerged-output HMMs for which output probabilities depend on past events. Wealso examine the influence of the architecture and parameters of the method interms of accuracies of rhythm transcription and voice separation and performcomparative evaluations with six other algorithms. Using MIDI recordings ofclassical piano pieces, we found that the proposed model outperformed othermethods by more than 12 points in the accuracy for polyrhythmic performancesand performed almost as good as the best one for non-polyrhythmic performances.This reveals the state-of-the-art methods of rhythm transcription for the firsttime in the literature. Publicly available source codes are also provided forfuture comparisons.

Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices