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

香港中文大學研究人員
替代計量分析
.

其它資訊
摘要Automatic 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.
著者Lufei Gao, Li Su, Yi-Hsuan Yang, Tan Lee
會議名稱2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
會議開始日05.03.2017
會議完結日09.03.2017
會議地點New Orleans
會議國家/地區美國
會議論文集題名2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
書名Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on
出版年份2017
月份3
頁次291 - 295
國際標準書號978-1-5090-4116-9
電子國際標準書號978-1-5090-4117-6
電子國際標準期刊號2379-190X
語言美式英語
關鍵詞Music information retrieval, spectral ftux, differential spectrogram, non-negative matrix factorization

上次更新時間 2020-02-12 於 01:09