國立虎尾科技大學 |

Improved Speech Enhancement Algorithm based on Generative Adversarial Networks.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Improved Speech Enhancement Algorithm based on Generative Adversarial Networks./
作者:	Wang, Kebei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	35 p.
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Information science. -
電子資源:	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:

561178
Information science.
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 Electrical engineering. $3 596380
653 $a Deep Learning
653 $a Generative adversarial networks
653 $a Speech enhancement
653 $a Speech denoising
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