Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network

• Published in: 2021 2nd International Workshop on Electronic communication and Artificial Intelligence, IWECAI 2021, Nanjing, China Vol. 1873; no. 1; p. 12034
• Main Authors: Lin, Xiongwei, Yang, Dongru, Mao, Yadong, Zhou, Lei, Zhao, Xiaobo, Lu, Shengguo
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Bristol IOP Publishing 01.04.2021
• Subjects: Algorithms; Intelligibility; Machine learning; Neural networks; Noise control; Noise reduction; Optimization; Physics; Sound; Speech
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1873/1/012034

Abstract To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor’s noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability.