Journal
IEEE ACCESS
Volume 5, Issue -, Pages 21193-21203Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2759142
Keywords
Speech denoising; mixed noise; sparsity; joint estimation; ADMM
Categories
Funding
- National Natural Science Foundation of China [61501072]
- Foundation and Advanced Research Projects of Chongqing Municipal Science and Technology Commission [cstc2015jcyjA40027, cstc2015jcyjA40040]
- Program for Changjiang Scholars and Innovative Research Team in University [1RT16R72]
- Special Fund of the Chongqing Key Laboratory
Ask authors/readers for more resources
The speech denoising problem in the presence of mixed impulsive and Gaussian noises is investigated by exploiting transform domains. To that end, the proposed noise suppression scheme is a cascaded form consisting of an impulsive noise suppression module and a Gaussian noise suppression module. For the impulsive noise reduction subsystem, in this paper, the noise is sparsely represented by the time domain, whereas short-time Fourier transform, wavelet transform, and wavelet synchrosqueezed transform are studied to provide sparse representations for the speech. By utilizing the transform domains, the speech recovery and the impulsive noise suppression are simultaneously achieved under an optimization framework. Subsequently, the alternating direction method of multipliers is used to solve 1-norm constrained optimization. In the Gaussian noise reduction subsystem, the Gaussian noise is suppressed by the famous Wiener filter in the transform domains as well. Numerical studies, including simulations and real data analysis, demonstrate the superior performance of the proposed scheme.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available