International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 83
Paper Published : 1216
No. of Authors : 3056
  Journal Paper

Paper Title
Context-Awareideal Ratio Mask Estimation for Speech Denoising using Deep Neural Networks

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


Author - Hannah Lee, Jaegyu Choi, Deokgyu Yun, Seung Ho Choi

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| Published on 2018-04-12
   
   
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