{"title":"线性预测分析中的窗口优化","authors":"W. Chu","doi":"10.1109/TSA.2003.818213","DOIUrl":null,"url":null,"abstract":"The autocorrelation method of linear prediction (LP) analysis relies on a window for data extraction. We propose an approach to optimize the window which is based on gradient-descent. It is shown that the optimized window can enhance the performance of LP-based speech coding algorithms; in most instances, improvement in performance comes at no additional computational cost, since it merely requires a window replacement.","PeriodicalId":13155,"journal":{"name":"IEEE Trans. Speech Audio Process.","volume":"24 1","pages":"626-635"},"PeriodicalIF":0.0000,"publicationDate":"2003-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Window optimization in linear prediction analysis\",\"authors\":\"W. Chu\",\"doi\":\"10.1109/TSA.2003.818213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The autocorrelation method of linear prediction (LP) analysis relies on a window for data extraction. We propose an approach to optimize the window which is based on gradient-descent. It is shown that the optimized window can enhance the performance of LP-based speech coding algorithms; in most instances, improvement in performance comes at no additional computational cost, since it merely requires a window replacement.\",\"PeriodicalId\":13155,\"journal\":{\"name\":\"IEEE Trans. Speech Audio Process.\",\"volume\":\"24 1\",\"pages\":\"626-635\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Trans. Speech Audio Process.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TSA.2003.818213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Trans. Speech Audio Process.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TSA.2003.818213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The autocorrelation method of linear prediction (LP) analysis relies on a window for data extraction. We propose an approach to optimize the window which is based on gradient-descent. It is shown that the optimized window can enhance the performance of LP-based speech coding algorithms; in most instances, improvement in performance comes at no additional computational cost, since it merely requires a window replacement.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
