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Improved Speech Enhancement Algorithm based on Generative Adversarial Networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Improved Speech Enhancement Algorithm based on Generative Adversarial Networks./
Author:	Wang, Kebei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	35 p.
Notes:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28643237
ISBN:	9798544278894

Improved Speech Enhancement Algorithm based on Generative Adversarial Networks.
Wang, Kebei.

Improved Speech Enhancement Algorithm based on Generative Adversarial Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 35 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.E.)--Iowa State University, 2021.

This item must not be sold to any third party vendors.

According to recent research, the techniques of speech enhancement have been increasingly improved to a quite high-level, especially for speech denoising problem. We need to blindly separate the speech audio signal from background noise. This task is challenging because the speech waveform and the noise waveform are superimposed, and there are no simple features that allow the separation of the two. In this thesis, we investigate possible improvements of using generative adversarial networks to perform speech de-noising.

ISBN: 9798544278894Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Deep Learning

Improved Speech Enhancement Algorithm based on Generative Adversarial Networks.
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300 $a 35 p.
500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: Wang, Zhengdao.
502 $a Thesis (M.E.)--Iowa State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a According to recent research, the techniques of speech enhancement have been increasingly improved to a quite high-level, especially for speech denoising problem. We need to blindly separate the speech audio signal from background noise. This task is challenging because the speech waveform and the noise waveform are superimposed, and there are no simple features that allow the separation of the two. In this thesis, we investigate possible improvements of using generative adversarial networks to perform speech de-noising.
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650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
650 4 $a Communication. $3 556422
650 4 $a Information science. $3 561178
653 $a Deep Learning
653 $a Generative adversarial networks
653 $a Speech enhancement
653 $a Speech denoising
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710 2 $a Iowa State University. $b Electrical and Computer Engineering. $3 1179387
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