Paper Title :Context-Awareideal Ratio Mask Estimation for Speech Denoising using Deep Neural Networks
Author :Hannah Lee, Jaegyu Choi, Deokgyu Yun, Seung Ho Choi
Article Citation :Hannah Lee ,Jaegyu Choi ,Deokgyu Yun ,Seung Ho Choi ,
(2018 ) " Context-Awareideal Ratio Mask Estimation for Speech Denoising using Deep Neural Networks " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 51-52,
Volume-6, Issue-2
Abstract : In this paper, we propose a context-aware approachfor speech denoising based on deep learning. In most studies,
single deep neural network(DNN) is trained in various noisy environments. However, large variations of noisy environments
can cause performance degradation. We develop environmentally-optimized models trained in each noisy environment,
which is effective in restoring clean speech spectra under different noisy conditions.In this research work, we first classify
noisy spectra through DNN models trained for classifying noise environment before noisy speech spectra are processed. We
apply the proposed method to the estimation of ideal ratio mask (IRM) that is used for denoising in spectral domain.
Experimental results showed that the proposed context-aware method performs better than that using non-context-awareness
model.
Keywords - Speech denoising, Context-awareness,Deep learning, Deep neural network,Ideal ratio mask
Type : Research paper
Published : Volume-6, Issue-2
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-11211
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Copyright: © Institute of Research and Journals
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Published on 2018-04-12 |
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