{"title":"二次型盲源分离创新","authors":"Zhenwei Shi, Zhanxing Zhu, X. Tan, Zhi-guo Jiang","doi":"10.1109/ICNC.2009.328","DOIUrl":null,"url":null,"abstract":"This paper proposes a blind source separation (BSS) method based on the quadratic form innovation of original sources, which includes linear predictability and energy (square) predictability as special cases. A simple algorithm is presented by minimizing a loss function of the quadratic form innovation. Simulations by source signals with linear or square temporal autocorrelations verify the efficient implementation of the proposed method.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"12 31","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quadratic Form Innovation to Blind Source Separation\",\"authors\":\"Zhenwei Shi, Zhanxing Zhu, X. Tan, Zhi-guo Jiang\",\"doi\":\"10.1109/ICNC.2009.328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a blind source separation (BSS) method based on the quadratic form innovation of original sources, which includes linear predictability and energy (square) predictability as special cases. A simple algorithm is presented by minimizing a loss function of the quadratic form innovation. Simulations by source signals with linear or square temporal autocorrelations verify the efficient implementation of the proposed method.\",\"PeriodicalId\":235382,\"journal\":{\"name\":\"2009 Fifth International Conference on Natural Computation\",\"volume\":\"12 31\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2009.328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2009.328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quadratic Form Innovation to Blind Source Separation
This paper proposes a blind source separation (BSS) method based on the quadratic form innovation of original sources, which includes linear predictability and energy (square) predictability as special cases. A simple algorithm is presented by minimizing a loss function of the quadratic form innovation. Simulations by source signals with linear or square temporal autocorrelations verify the efficient implementation of the proposed method.