Projects per year
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.
|Title of host publication||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - 16 Jun 2017|
|Event||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States|
Duration: 5 Mar 2017 → 9 Mar 2017
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017|
|Period||5/03/17 → 9/03/17|
- Deep neural network
- phase information
FingerprintDive into the research topics of 'Fully complex deep neural network for phase-incorporating monaural source separation'. Together they form a unique fingerprint.
- 1 Finished
1/08/16 → 31/07/17