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AN ATTACK/DECAY MODEL FOR PIANO TRANSCRIPTION
"... ABSTRACT We demonstrate that piano transcription performance for a known piano can be improved by explicitly modelling piano acoustical features. The proposed method is based on non-negative matrix factorisation, with the following three refinements: (1) introduction of attack and harmonic decay co ..."
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ABSTRACT We demonstrate that piano transcription performance for a known piano can be improved by explicitly modelling piano acoustical features. The proposed method is based on non-negative matrix factorisation, with the following three refinements: (1) introduction of attack and harmonic decay components; (2) use of a spike-shaped note activation that is shared by these components; (3) modelling the harmonic decay with an exponential function. Transcription is performed in a supervised way, with the training and test datasets produced by the same piano. First we train parameters for the attack and decay components on isolated notes, then update only the note activations for transcription. Experiments show that the proposed model achieves 82% on note-wise and 79% on frame-wise F-measures on the 'ENSTDkCl' subset of the MAPS database, outperforming the current published state of the art.
PIANO MUSIC TRANSCRIPTIONWITH FAST CONVOLUTIONAL SPARSE CODING
"... Automatic music transcription (AMT) is the process of converting an acoustic musical signal into a symbolic musical representation, such as a MIDI file, which contains the pitches, the onsets and offsets of the notes and, possibly, their dynamics and sources (i.e., instru-ments). Most existing algor ..."
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Automatic music transcription (AMT) is the process of converting an acoustic musical signal into a symbolic musical representation, such as a MIDI file, which contains the pitches, the onsets and offsets of the notes and, possibly, their dynamics and sources (i.e., instru-ments). Most existing algorithms for AMT operate in the frequency domain, which introduces the well known time/frequency resolution trade-off of the Short Time Fourier Transform and its variants. In this paper, we propose a time-domain transcription algorithm based on an efficient convolutional sparse coding algorithm in an instrument-specific scenario, i.e., the dictionary is trained and tested on the same piano. The proposed method outperforms a current state-of-the-art AMT method by over 26 % in F-measure, achieving a median F-measure of 93.6%, and drastically increases both time and frequency resolutions, especially for the lowest octaves of the piano keyboard.
