{"title":"语音信号混合的去噪与信号分离","authors":"S. Nordholm, H. H. Dam","doi":"10.1109/ICCAIS56082.2022.9990518","DOIUrl":null,"url":null,"abstract":"This paper contributes to the dereverberation and signal separation problem of speech signal mixtures in reverberant environments by comparing the performance of different subband transform techniques, namely PolyPhase Filter Banks (PPFB) and weighted overlap-add short-term Fourier transform (WOLA STFT). The subband techniques allow large deconvolution problem to be subdivided into many smaller problems, which are feasible to solve. The critical finding is that the PPFB has better performance measures when compared with the WOLA STFT while having a lower computational complexity due to a lower subsampling rate. From the evaluation study, it can be seen that both the signal separation and the de-reverberation perform well even for high levels of background noise (SNR=0dB).","PeriodicalId":273404,"journal":{"name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dereverberation and Signal Separation of Speech Signal Mixtures\",\"authors\":\"S. Nordholm, H. H. Dam\",\"doi\":\"10.1109/ICCAIS56082.2022.9990518\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper contributes to the dereverberation and signal separation problem of speech signal mixtures in reverberant environments by comparing the performance of different subband transform techniques, namely PolyPhase Filter Banks (PPFB) and weighted overlap-add short-term Fourier transform (WOLA STFT). The subband techniques allow large deconvolution problem to be subdivided into many smaller problems, which are feasible to solve. The critical finding is that the PPFB has better performance measures when compared with the WOLA STFT while having a lower computational complexity due to a lower subsampling rate. From the evaluation study, it can be seen that both the signal separation and the de-reverberation perform well even for high levels of background noise (SNR=0dB).\",\"PeriodicalId\":273404,\"journal\":{\"name\":\"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAIS56082.2022.9990518\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS56082.2022.9990518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dereverberation and Signal Separation of Speech Signal Mixtures
This paper contributes to the dereverberation and signal separation problem of speech signal mixtures in reverberant environments by comparing the performance of different subband transform techniques, namely PolyPhase Filter Banks (PPFB) and weighted overlap-add short-term Fourier transform (WOLA STFT). The subband techniques allow large deconvolution problem to be subdivided into many smaller problems, which are feasible to solve. The critical finding is that the PPFB has better performance measures when compared with the WOLA STFT while having a lower computational complexity due to a lower subsampling rate. From the evaluation study, it can be seen that both the signal separation and the de-reverberation perform well even for high levels of background noise (SNR=0dB).