Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 21, Issue 1, Pages 65-68Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2013.2291240
Keywords
Deep neural networks; noise reduction; regression model; speech enhancement
Categories
Funding
- National Nature Science Foundation of China [61273264, 61305002]
- National 973 program of China [2012CB326405]
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This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be separated from the background noises successfully without the annoying musical artifact commonly observed in conventional speech enhancement algorithms. A series of pilot experiments were conducted under multi-condition training with more than 100 hours of simulated speech data, resulting in a good generalization capability even in mismatched testing conditions. When compared with the logarithmic minimum mean square error approach, the proposed DNN-based algorithm tends to achieve significant improvements in terms of various objective quality measures. Furthermore, in a subjective preference evaluation with 10 listeners, 76.35% of the subjects were found to prefer DNN-based enhanced speech to that obtained with other conventional technique.
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