A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech

被引：31

作者：

Valin, Jean-Marc ^{[1
]}

Isik, Umut ^{[2
]}

Phansalkar, Neerad ^{[2
]}

Giri, Ritwik ^{[2
]}

Helwani, Karim ^{[2
]}

Krishnaswamy, Arvindh ^{[2
]}

机构：

[1] Amazon Web Serv, Toronto, ON, Canada

[2] Amazon Web Serv, Seattle, WA USA

来源：

INTERSPEECH 2020 | 2020年

关键词：

speech enhancement; pitch filtering; postfilter; MASKING; NOISE;

D O I：

10.21437/Interspeech.2020-2730

中图分类号：

R36 [病理学]; R76 [耳鼻咽喉科学];

学科分类号：

100104 ; 100213 ;

摘要：

Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of these new techniques operate directly in the the short-time Fourier transform (STFT) domain, resulting in a high computational complexity. In this work, we propose PercepNet, an efficient approach that relies on human perception of speech by focusing on the spectral envelope and on the periodicity of the speech. We demonstrate high-quality, real-time enhancement of fullband (48 kHz) speech with less than 5% of a CPU core.

引用

页码：2482 / 2486

页数：5

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