Polyphonic Piano Transcription with a Note-Based Music Language Model

Author： Wang Qi Zhou Ruohua Yan Yonghong

Publisher： MDPI

E-ISSN： 2076-3417|8|3|470-470

ISSN： 2076-3417

Source： Applied Sciences, Vol.8, Iss.3, 2018-03, pp. : 470-470

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Abstract

This paper proposes a note-based music language model (MLM) for improving note-level polyphonic piano transcription. The MLM is based on the recurrent structure, which could model the temporal correlations between notes in music sequences. To combine the outputs of the note-based MLM and acoustic model directly, an integrated architecture is adopted in this paper. We also propose an inference algorithm, in which the note-based MLM is used to predict notes at the blank onsets in the thresholding transcription results. The experimental results show that the proposed inference algorithm improves the performance of note-level transcription. We also observe that the combination of the restricted Boltzmann machine (RBM) and recurrent structure outperforms a single recurrent neural network (RNN) or long short-term memory network (LSTM) in modeling the high-dimensional note sequences. Among all the MLMs, LSTM-RBM helps the system yield the best results on all evaluation metrics regardless of the performance of acoustic models.