{"title":"基于stft域降噪的语音失真加权单通道维纳滤波器","authors":"Jie Zhang, Rui Tao, Lirong Dai","doi":"10.1109/SSP53291.2023.10208040","DOIUrl":null,"url":null,"abstract":"In this work, we focus on the single-channel noise reduction (NR) in the short-time Fourier transform (STFT) domain from the traditional signal processing perspective. As conventional single-channel NR methods suffer from a serious speech distortion (SD), we propose an SD weighted single-channel Wiener filter (SDW-SWF), where an auxiliary parameter µ is exploited to trade-off the SD and residual noise variance. In the subspace, the obtained SDW-SWF can be formulated as a function of µ and a set of generalized eigenvectors of correlation matrices. In addition, we theoretically analyze the impacts of the trade-off factor and the rank on the SD, residual noise power and the output signal-to-noise ratio (SNR). Finally, numerical results validate the effectiveness of the proposed method, exhibiting a consistency with the theoretical findings. It can be concluded that the SDW-SWF approach enables more degrees-of-freedom to improve the speech intelligibility at a sacrifice of SNR.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Speech Distortion Weighted Single-Channel Wiener Filter Based STFT-Domain Noise Reduction\",\"authors\":\"Jie Zhang, Rui Tao, Lirong Dai\",\"doi\":\"10.1109/SSP53291.2023.10208040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we focus on the single-channel noise reduction (NR) in the short-time Fourier transform (STFT) domain from the traditional signal processing perspective. As conventional single-channel NR methods suffer from a serious speech distortion (SD), we propose an SD weighted single-channel Wiener filter (SDW-SWF), where an auxiliary parameter µ is exploited to trade-off the SD and residual noise variance. In the subspace, the obtained SDW-SWF can be formulated as a function of µ and a set of generalized eigenvectors of correlation matrices. In addition, we theoretically analyze the impacts of the trade-off factor and the rank on the SD, residual noise power and the output signal-to-noise ratio (SNR). Finally, numerical results validate the effectiveness of the proposed method, exhibiting a consistency with the theoretical findings. It can be concluded that the SDW-SWF approach enables more degrees-of-freedom to improve the speech intelligibility at a sacrifice of SNR.\",\"PeriodicalId\":296346,\"journal\":{\"name\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP53291.2023.10208040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP53291.2023.10208040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Speech Distortion Weighted Single-Channel Wiener Filter Based STFT-Domain Noise Reduction
In this work, we focus on the single-channel noise reduction (NR) in the short-time Fourier transform (STFT) domain from the traditional signal processing perspective. As conventional single-channel NR methods suffer from a serious speech distortion (SD), we propose an SD weighted single-channel Wiener filter (SDW-SWF), where an auxiliary parameter µ is exploited to trade-off the SD and residual noise variance. In the subspace, the obtained SDW-SWF can be formulated as a function of µ and a set of generalized eigenvectors of correlation matrices. In addition, we theoretically analyze the impacts of the trade-off factor and the rank on the SD, residual noise power and the output signal-to-noise ratio (SNR). Finally, numerical results validate the effectiveness of the proposed method, exhibiting a consistency with the theoretical findings. It can be concluded that the SDW-SWF approach enables more degrees-of-freedom to improve the speech intelligibility at a sacrifice of SNR.