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Author:

Cheng, Rui (Cheng, Rui.) | Bao, Changchun (Bao, Changchun.) (Scholars:鲍长春)

Indexed by:

EI

Abstract:

Speech enhancement is a vital technology for reducing the noise in speech communication. Most speech enhancement methods only estimate magnitude spectrum of clean speech from noisy speech and combine noisy phase spectrum to recover the enhanced speech. In this paper, considering the importance of recovering the phase of clean speech in speech enhancement, a phase recovery method of speech is proposed by combining phase unwrapping and deep neural network (DNN). By integrating the recovered phase of clean speech into conventional magnitude enhancement methods, the performance is improved effectively. The verification is conducted by several types of noises at different signal-to-noise ratio (SNR) levels. The experimental results also confirmed that the recovered phase of clean speech resulted in an obvious improvement on the speech quality and intelligibility compared to the noisy phase. © 2019 IEEE.

Keyword:

Speech intelligibility Speech communication Speech enhancement Signal to noise ratio Deep neural networks Recovery

Author Community:

  • [ 1 ] [Cheng, Rui]Speech and Audio Signal Processing Laboratory, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Bao, Changchun]Speech and Audio Signal Processing Laboratory, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

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Year: 2019

Page: 884-889

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 15

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