{"title":"基于进化方法的语音增强算法自适应调谐","authors":"Ryan LeBlanc, S. Selouani","doi":"10.1109/CogMI48466.2019.00012","DOIUrl":null,"url":null,"abstract":"In this paper a novel approach to speech enhancement algorithm tuning is presented. A self-adaptive tuning method was developed using an evolutionary optimization algorithm. Through this new approach, an improvement on the multiband spectral subtraction method was obtained by using a genetic algorithm to adaptively tune the algorithm's parameters for the speech being processed. Experimental tests using objective quality and intelligibility measures showed that the proposed artificial intelligence-based method offers better noise reduction with less signal distortion when compared to existing speech enhancement methods.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Self-Adaptive Tuning for Speech Enhancement Algorithm Based on Evolutionary Approach\",\"authors\":\"Ryan LeBlanc, S. Selouani\",\"doi\":\"10.1109/CogMI48466.2019.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a novel approach to speech enhancement algorithm tuning is presented. A self-adaptive tuning method was developed using an evolutionary optimization algorithm. Through this new approach, an improvement on the multiband spectral subtraction method was obtained by using a genetic algorithm to adaptively tune the algorithm's parameters for the speech being processed. Experimental tests using objective quality and intelligibility measures showed that the proposed artificial intelligence-based method offers better noise reduction with less signal distortion when compared to existing speech enhancement methods.\",\"PeriodicalId\":116160,\"journal\":{\"name\":\"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)\",\"volume\":\"313 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CogMI48466.2019.00012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CogMI48466.2019.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-Adaptive Tuning for Speech Enhancement Algorithm Based on Evolutionary Approach
In this paper a novel approach to speech enhancement algorithm tuning is presented. A self-adaptive tuning method was developed using an evolutionary optimization algorithm. Through this new approach, an improvement on the multiband spectral subtraction method was obtained by using a genetic algorithm to adaptively tune the algorithm's parameters for the speech being processed. Experimental tests using objective quality and intelligibility measures showed that the proposed artificial intelligence-based method offers better noise reduction with less signal distortion when compared to existing speech enhancement methods.