Speech Denoising Using Transform Domains in the Presence of Impulsive and Gaussian Noises
Author(s) -
Hongqing Liu,
Ruibo Zhang,
Yi Zhou,
Xiaorong Jing,
Trieu-Kien Truong
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2759142
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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 11-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.
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