Sebastian Böck, Markus Schedl,
"Polyphonic Piano Note Transcription with Recurrent Neural Networks."
: Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), 2012
Original Titel:
Polyphonic Piano Note Transcription with Recurrent Neural Networks.
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
Original Kurzfassung:
In this paper a new approach for polyphonic piano note onset
transcription is presented. It is based on a recurrent neural
network to simultaneously detect the onsets and the pitches
of the notes from spectral features. Long Short-Term Memory
units are used in a bidirectional neural network to model
the context of the notes. The use of a single regression output
layer instead of the often used one-versus-all classification
approach enables the system to significantly lower the
number of erroneous note detections. Evaluation is based
on common test sets and shows exceptional temporal precision
combined with a significant boost in note transcription
performance compared to current state-of-the-art approaches.
The system is trained jointly with various synthesized piano
instruments and real piano recordings and thus generalizes
much better than existing systems.
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