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Automatic transcription of piano music by sparse representation of magnitude spectra

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3 Author(s)
Cheng-Te Lee ; National Taiwan University, Taiwan ; Yi-Hsuan Yang ; Homer Chen

Assuming that the waveforms of piano notes are pre-stored and that the magnitude spectrum of a piano signal segment can be represented as a linear combination of the magnitude spectra of the pre-stored piano waveforms, we formulate the automatic transcription of polyphonic piano music as a sparse representation problem. First, the note candidates of the piano signal segment are found by using heuristic rules. Then, the sparse representation problem is solved by l1-regularized minimization, followed by temporal smoothing the frame-level results based on hidden Markov models. Evaluation against three state-of-the-art systems using ten classical music recordings of a real piano is performed to show the performance improvement of the proposed system.

Published in:

2011 IEEE International Conference on Multimedia and Expo

Date of Conference:

11-15 July 2011