Automatic transcription of piano music by sparse representation of magnitude spectra
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.