Towards Complete Polyphonic Music Transcription: Integrating Multi-Pitch Detection and Rhythm Quantization
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Most work on automatic transcription produces "piano roll" data with no musical interpretation of the rhythm or pitches. We present a polyphonic transcription method that converts a music audio signal into a human-readable musical score, by integrating multi-pitch detection and rhythm quantization methods. This integration is made difficult by the fact that the multi-pitch detection produces erroneous notes such as extra notes and introduces timing errors that are added to temporal deviations due to musical expression. Thus, we propose a rhythm quantization method that can remove extra notes by extending the metrical hidden Markov model and optimize the model parameters. We also improve the note-tracking process of multi-pitch detection by refining the treatment of repeated notes and adjustment of onset times. Finally, we propose evaluation measures for transcribed scores. Systematic evaluations on commonly used classical piano data show that these treatments improve the performance of transcription, which can be used as benchmarks for further studies.