Fully complex deep neural network for phase-incorporating monaural source separation

Yuan Shan Lee, Chien Yao Wang, Shu Fan Wang, Jia Ching Wang, Chung Hsien Wu

研究成果: Conference contribution

31 引文 斯高帕斯(Scopus)

摘要

Deep neural network (DNN) have become a popular means of separating a target source from a mixed signal. Most of DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the input signal. However, recent studies have revealed that incorporating phase information can improve the quality of separated sources. To estimate simultaneously the magnitude and the phase of STFT coefficients, this work paper developed a fully complex-valued deep neural network (FCDNN) that learns the nonlinear mapping from complex-valued STFT coefficients of a mixture to sources. In addition, to reinforce the sparsity of the estimated spectra, a sparse penalty term is incorporated into the objective function of the FCDNN. Finally, the proposed method is applied to singing source separation. Experimental results indicate that the proposed method outperforms the state-of-the-art DNN-based methods.

原文English
主出版物標題2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面281-285
頁數5
ISBN(電子)9781509041176
DOIs
出版狀態Published - 2017 6月 16
事件2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
持續時間: 2017 3月 52017 3月 9

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(列印)1520-6149

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
國家/地區United States
城市New Orleans
期間17-03-0517-03-09

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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