{"title":"On the use of LSF intermodel interlacing property for spectral quantization","authors":"Mi Suk Lee, H. Kim, S. Choi, Hwang-Soo Lee","doi":"10.1109/SCFT.1999.781478","DOIUrl":null,"url":null,"abstract":"The line spectral frequencies (LSFs) extracted from successive analysis orders are interlaced with each other. This intermodel interlacing property gives a new relationship between the closeness of LSFs and their spectral sensitivities, which enables us to propose a weighting function for LSF distortion measurement. By applying the proposed weighting function to an LSF quantizer, we can achieve better performance than when using the conventional heuristic functions. Moreover, the complexity of the proposed weighting function is much lower than that of the optimal weighting function, while their performances are almost the same.","PeriodicalId":372569,"journal":{"name":"1999 IEEE Workshop on Speech Coding Proceedings. Model, Coders, and Error Criteria (Cat. No.99EX351)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1999 IEEE Workshop on Speech Coding Proceedings. Model, Coders, and Error Criteria (Cat. No.99EX351)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCFT.1999.781478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The line spectral frequencies (LSFs) extracted from successive analysis orders are interlaced with each other. This intermodel interlacing property gives a new relationship between the closeness of LSFs and their spectral sensitivities, which enables us to propose a weighting function for LSF distortion measurement. By applying the proposed weighting function to an LSF quantizer, we can achieve better performance than when using the conventional heuristic functions. Moreover, the complexity of the proposed weighting function is much lower than that of the optimal weighting function, while their performances are almost the same.