{"title":"Efficient representation of head-related transfer functions in subbands","authors":"D. Marelli, Robert Baumgartner, P. Majdak","doi":"10.5281/ZENODO.43899","DOIUrl":null,"url":null,"abstract":"Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology. We propose three algorithms for representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. These algorithms can be combined to achieve different design objectives. In the first algorithm, the choice of FBs is fixed, and a sparse approximation procedure minimizes the complexity of the transfer matrix associated to each HRTF. The other two algorithms jointly optimize the FBs and transfer matrices. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments show that the proposed methods offer significant computational savings when compared with other available approaches.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.43899","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Head-related transfer functions (HRTFs) describe the acoustic filtering of incoming sounds by the human morphology. We propose three algorithms for representing HRTFs in subbands, i.e., as an analysis filterbank (FB) followed by a transfer matrix and a synthesis FB. These algorithms can be combined to achieve different design objectives. In the first algorithm, the choice of FBs is fixed, and a sparse approximation procedure minimizes the complexity of the transfer matrix associated to each HRTF. The other two algorithms jointly optimize the FBs and transfer matrices. The first variant aims at minimizing the complexity of the transfer matrices, while the second one does it for the FBs. Numerical experiments show that the proposed methods offer significant computational savings when compared with other available approaches.