UNetGAN: A Robust Speech Enhancement Approach in Time Domain for Extremely Low Signal-to-noise Ratio Condition

Autor: Zhiyu Wang, Batushiren, Xiangdong Su, Xiang Hao, Hui Zhang
Rok vydání: 2020
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Sound (cs.SD)
business.industry
Computer science
Speech recognition
Deep learning
02 engineering and technology
Intelligibility (communication)
Computer Science - Sound
Speech enhancement
030507 speech-language pathology & audiology
03 medical and health sciences
Signal-to-noise ratio
Audio and Speech Processing (eess.AS)
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
020201 artificial intelligence & image processing
Time domain
Artificial intelligence
Electrical Engineering and Systems Science - Signal Processing
0305 other medical science
business
PESQ
Electrical Engineering and Systems Science - Audio and Speech Processing
Zdroj: INTERSPEECH
DOI: 10.48550/arxiv.2010.15521
Popis: Speech enhancement at extremely low signal-to-noise ratio (SNR) condition is a very challenging problem and rarely investigated in previous works. This paper proposes a robust speech enhancement approach (UNetGAN) based on U-Net and generative adversarial learning to deal with this problem. This approach consists of a generator network and a discriminator network, which operate directly in the time domain. The generator network adopts a U-Net like structure and employs dilated convolution in the bottleneck of it. We evaluate the performance of the UNetGAN at low SNR conditions (up to -20dB) on the public benchmark. The result demonstrates that it significantly improves the speech quality and substantially outperforms the representative deep learning models, including SEGAN, cGAN fo SE, Bidirectional LSTM using phase-sensitive spectrum approximation cost function (PSA-BLSTM) and Wave-U-Net regarding Short-Time Objective Intelligibility (STOI) and Perceptual evaluation of speech quality (PESQ).
Comment: Published in Interspeech 2019
Databáze: OpenAIRE
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