{"title":"基于递归最小二乘滤波器的自适应噪声消除系统","authors":"Y. Beltrán-Gómez, J. Gómez-Rojas, R. Linero-Ramos","doi":"10.22463/0122820X.2435","DOIUrl":null,"url":null,"abstract":"In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6 is reached, and with this it makes possible a low-cost implementation.","PeriodicalId":20991,"journal":{"name":"Respuestas","volume":"13 1","pages":"7"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Noise Cancellation System Using a Recursive Least Squares Filter\",\"authors\":\"Y. Beltrán-Gómez, J. Gómez-Rojas, R. Linero-Ramos\",\"doi\":\"10.22463/0122820X.2435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6 is reached, and with this it makes possible a low-cost implementation.\",\"PeriodicalId\":20991,\"journal\":{\"name\":\"Respuestas\",\"volume\":\"13 1\",\"pages\":\"7\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Respuestas\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22463/0122820X.2435\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Respuestas","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22463/0122820X.2435","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Noise Cancellation System Using a Recursive Least Squares Filter
In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6 is reached, and with this it makes possible a low-cost implementation.