Polyphonic piano note transcription with non-negative matrix factorization of differential spectrogram
Refereed conference paper presented and published in conference proceedings

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AbstractAutomatic music transcription is usually approached by using a time-frequency (TF) representation such as the short-time Fourier transform (STFT) spectrogram or the constant-Q transform. In this paper, we propose a novel yet simple TF representation that capitalizes the effectiveness of spectral flux features in highlighting note onset times. We refer to this representation as the differential spectrogram and investigate its usefulness for note-level piano transcription using two different non-negative matrix factorization (NMF) algorithms. Experiments on the MAPS ENSTDkCl dataset validate the advantages of the differential spectrogram over the STFT spectrogram for this task. Moreover, by adapting a state-of-the-art convolutional NMF algorithm with the differential spectrogram, we can achieve even better accuracy than the state-of-the-art on this dataset. Our analysis shows that the new representation suppresses unwanted TF patterns and performs particularly well in improving the recall rate.
All Author(s) ListLufei Gao, Li Su, Yi-Hsuan Yang, Tan Lee
Name of Conference2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Start Date of Conference05/03/2017
End Date of Conference09/03/2017
Place of ConferenceNew Orleans
Country/Region of ConferenceUnited States of America
Proceedings Title2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Book titleAcoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on
Pages291 - 295
LanguagesEnglish-United States
KeywordsMusic information retrieval, spectral ftux, differential spectrogram, non-negative matrix factorization

Last updated on 2020-18-10 at 00:53