{"title":"改进了正交源的识别性能","authors":"S. Grigis, A. Holobar, D. Zazula","doi":"10.1109/EURCON.2003.1248171","DOIUrl":null,"url":null,"abstract":"This paper deals with the problem of recognition of multiple orthogonal sources buried in highly superimposed observations. The known blind source separation (BSS) approach is upgraded with a separation of sources using a classification procedure. Single source contributions are looked for in spatial time-frequency distribution (STFD) of the observed signals. The classification is based on STFD matrices which are grouped in the orthogonal and similar classes. The resulting separation algorithm outperforms other known approaches, as well in accuracy as by lower computational complexity.","PeriodicalId":337983,"journal":{"name":"The IEEE Region 8 EUROCON 2003. Computer as a Tool.","volume":"12 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved recognition performance for orthogonal sources\",\"authors\":\"S. Grigis, A. Holobar, D. Zazula\",\"doi\":\"10.1109/EURCON.2003.1248171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper deals with the problem of recognition of multiple orthogonal sources buried in highly superimposed observations. The known blind source separation (BSS) approach is upgraded with a separation of sources using a classification procedure. Single source contributions are looked for in spatial time-frequency distribution (STFD) of the observed signals. The classification is based on STFD matrices which are grouped in the orthogonal and similar classes. The resulting separation algorithm outperforms other known approaches, as well in accuracy as by lower computational complexity.\",\"PeriodicalId\":337983,\"journal\":{\"name\":\"The IEEE Region 8 EUROCON 2003. Computer as a Tool.\",\"volume\":\"12 12\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The IEEE Region 8 EUROCON 2003. Computer as a Tool.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EURCON.2003.1248171\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The IEEE Region 8 EUROCON 2003. Computer as a Tool.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EURCON.2003.1248171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved recognition performance for orthogonal sources
This paper deals with the problem of recognition of multiple orthogonal sources buried in highly superimposed observations. The known blind source separation (BSS) approach is upgraded with a separation of sources using a classification procedure. Single source contributions are looked for in spatial time-frequency distribution (STFD) of the observed signals. The classification is based on STFD matrices which are grouped in the orthogonal and similar classes. The resulting separation algorithm outperforms other known approaches, as well in accuracy as by lower computational complexity.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
