{"title":"列车轮轨声信号的降噪研究","authors":"Qian Wang, Li-Juan Zhou, Q. Chen","doi":"10.1145/3036290.3036327","DOIUrl":null,"url":null,"abstract":"Based on a new algorithm that combines spectral subtraction of multitaper estimation with signal self-correlation analysis, wheel-rail sound signal has been theoretically processed in this article. The simulation results show that the new algorithm results in better signal audio-visual and de-noising effects than the individual spectral subtraction of mulitaper without self-correlation one.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"6 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on the De-Noising of Train Wheel-Rail Sound Signal\",\"authors\":\"Qian Wang, Li-Juan Zhou, Q. Chen\",\"doi\":\"10.1145/3036290.3036327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on a new algorithm that combines spectral subtraction of multitaper estimation with signal self-correlation analysis, wheel-rail sound signal has been theoretically processed in this article. The simulation results show that the new algorithm results in better signal audio-visual and de-noising effects than the individual spectral subtraction of mulitaper without self-correlation one.\",\"PeriodicalId\":109559,\"journal\":{\"name\":\"International Conference on Machine Learning and Soft Computing\",\"volume\":\"6 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Machine Learning and Soft Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3036290.3036327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Machine Learning and Soft Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3036290.3036327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on the De-Noising of Train Wheel-Rail Sound Signal
Based on a new algorithm that combines spectral subtraction of multitaper estimation with signal self-correlation analysis, wheel-rail sound signal has been theoretically processed in this article. The simulation results show that the new algorithm results in better signal audio-visual and de-noising effects than the individual spectral subtraction of mulitaper without self-correlation one.
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