{"title":"一种基于听觉感知的改进LSA-MMSE语音增强方法","authors":"L. Gong, Changxing Chen, Qi Chen, Haoxiang Xu","doi":"10.1109/FITME.2008.53","DOIUrl":null,"url":null,"abstract":"Gain function of traditional enhancement algorithm is to estimate every signal spectral component, therefore, this introduce relatively more speech distortion. To improve the effect of speech enhancement at low signal-to-noise ratio (SNR), this paper proposed a optimal speech enhancement scheme. Based on auditory perception properties, no estimator for noise masked spectrum and classical enhancement estimator for noise unmasked spectrum. Then a speech signal estimator is proposed as a weighted sum of the individual estimator in each state, where the weight is related with noise masked probability. Compared with Viragpsilas method and LSA-MMSE estimator, the proposed estimator can suppress the residual noise effectively while keep smaller speech distortion especially at low SNR.","PeriodicalId":218182,"journal":{"name":"2008 International Seminar on Future Information Technology and Management Engineering","volume":"212 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Improved LSA-MMSE Speech Enhancement Approach Based on Auditory Perception\",\"authors\":\"L. Gong, Changxing Chen, Qi Chen, Haoxiang Xu\",\"doi\":\"10.1109/FITME.2008.53\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gain function of traditional enhancement algorithm is to estimate every signal spectral component, therefore, this introduce relatively more speech distortion. To improve the effect of speech enhancement at low signal-to-noise ratio (SNR), this paper proposed a optimal speech enhancement scheme. Based on auditory perception properties, no estimator for noise masked spectrum and classical enhancement estimator for noise unmasked spectrum. Then a speech signal estimator is proposed as a weighted sum of the individual estimator in each state, where the weight is related with noise masked probability. Compared with Viragpsilas method and LSA-MMSE estimator, the proposed estimator can suppress the residual noise effectively while keep smaller speech distortion especially at low SNR.\",\"PeriodicalId\":218182,\"journal\":{\"name\":\"2008 International Seminar on Future Information Technology and Management Engineering\",\"volume\":\"212 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Seminar on Future Information Technology and Management Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FITME.2008.53\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Seminar on Future Information Technology and Management Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FITME.2008.53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved LSA-MMSE Speech Enhancement Approach Based on Auditory Perception
Gain function of traditional enhancement algorithm is to estimate every signal spectral component, therefore, this introduce relatively more speech distortion. To improve the effect of speech enhancement at low signal-to-noise ratio (SNR), this paper proposed a optimal speech enhancement scheme. Based on auditory perception properties, no estimator for noise masked spectrum and classical enhancement estimator for noise unmasked spectrum. Then a speech signal estimator is proposed as a weighted sum of the individual estimator in each state, where the weight is related with noise masked probability. Compared with Viragpsilas method and LSA-MMSE estimator, the proposed estimator can suppress the residual noise effectively while keep smaller speech distortion especially at low SNR.