{"title":"Hrirs' Adaptive Non-Linear Approximation Model Based on Wavelet Transformation","authors":"J. Zhang, Zhen-yang Wu","doi":"10.1109/MMSP.2005.248551","DOIUrl":null,"url":null,"abstract":"During the study of spatial hearing, it is requisite to consider how to properly model the acoustical characteristics of HRTFs (head-related transfer functions: HRTFs) or HRIRs (head-related impulse responses: HRIRs) corresponding to certain positions. In our work, we managed to carry through adaptive non-linear approximation in the field of wavelet transformation. The results show that, the HRIRs' adaptive nonlinear approximation model is a more effective data reduction model, faster and averagely 5 dB better than the traditional PCA (Karhunen-Loeve transform) model based on relative error","PeriodicalId":191719,"journal":{"name":"2005 IEEE 7th Workshop on Multimedia Signal Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2005 IEEE 7th Workshop on Multimedia Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP.2005.248551","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
During the study of spatial hearing, it is requisite to consider how to properly model the acoustical characteristics of HRTFs (head-related transfer functions: HRTFs) or HRIRs (head-related impulse responses: HRIRs) corresponding to certain positions. In our work, we managed to carry through adaptive non-linear approximation in the field of wavelet transformation. The results show that, the HRIRs' adaptive nonlinear approximation model is a more effective data reduction model, faster and averagely 5 dB better than the traditional PCA (Karhunen-Loeve transform) model based on relative error