Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82913
Title: On the preprocessing and postprocessing of HRTF individualization based on sparse representation of anthropometric features
Authors: He, Jianjun
Gan, Woon-Seng
Tan, Ee-Leng
Keywords: 3D audio
HRTF individualization
Head-related transfer function (HRTF)
Anthropometry
Issue Date: 2015
Source: He, J., Gan, W.-S., & Tan, E.-L. (2015). On the preprocessing and postprocessing of HRTF individualization based on sparse representation of anthropometric features. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 639-643.
Abstract: Individualization of head-related transfer functions (HRTFs) can be realized using the person's anthropometry with a pretrained model. This model usually establishes a direct linear or non-linear mapping from anthropometry to HRTFs in the training database. Due to the complex relation between anthropometry and HRTFs, the accuracy of this model depends heavily on the correct selection of the anthropometric features. To alleviate this problem and improve the accuracy of HRTF individualization, an indirect HRTF individualization framework was proposed recently, where HRTFs are synthesized using a sparse representation trained from the anthropometric features. In this paper, we extend their study on this framework by investigating the effects of different preprocessing and postprocessing methods on HRTF individualization. Our experimental results showed that preprocessing and postprocessing methods are crucial for achieving accurate HRTF individualization.
URI: https://hdl.handle.net/10356/82913
http://hdl.handle.net/10220/40370
DOI: 10.1109/ICASSP.2015.7178047
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICASSP.2015.7178047].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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