A Regression Approach to Speech Enhancement Based on Deep Neural Networks

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide...

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Veröffentlicht in:	IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2015-01, Vol.23 (1), p.7-19
Hauptverfasser:	Xu, Yong, Du, Jun, Dai, Li-Rong, Lee, Chin-Hui
Format:	Artikel
Sprache:	eng
Schlagworte:	Deep neural networks (DNNs) dropout global variance equalization IEEE transactions Noise noise aware training Noise measurement noise reduction nonstationary noise Speech Speech enhancement Training
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