@inproceedings{16adccfcb6b542009dd0a1a947ac39d2,
title = "Dual-Masking Wind Noise Reduction System Based on Recurrent Neural Network",
abstract = "In this paper, we adopt the architecture of permutation invariant training (PIT) model. We take advantage of the dual mask features of the speech separation architecture and combine the results of the two masks to synthesize a better signal with a specific ratio. We use bidirectional gated recurrent unit (BGRU) to find appropriate weights for the features after short time Fourier transform (STFT). A mask finds the signal you want to keep. Another mask finds the unwanted signals. Compared with the traditional method for eliminating wind noise, our proposed method can achieve better noise reduction for non-stationary and non-periodic wind noise.",
keywords = "Deep learning, Dual mask, Noise reduction, Speech separation, Wind noise",
author = "Liu, {Wei Hung} and Lai, {Yen Ting} and Liang, {Kai Wen} and Wang, {Jia Ching} and Chang, {Pao Chi}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 ; Conference date: 16-11-2021 Through 19-11-2021",
year = "2021",
doi = "10.1109/ISPACS51563.2021.9650991",
language = "???core.languages.en_GB???",
series = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems",
}