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Modeling of Individual HRTFs Based on Spatial Principal Component Analysis
Zhang, Mengfan1; Ge, Zhongshu1; Liu TJ(刘铁军)2; Wu XH(吴玺宏)1; Qu TS(曲天书)1
Department水下机器人研究室
Corresponding AuthorQu, Tianshu(qutianshu@pku.edu.cn)
Source PublicationIEEE/ACM Transactions on Audio Speech and Language Processing
ISSN2329-9290
2020
Volume28Pages:785-797
Indexed BySCI ; EI
EI Accession number20200708179413
WOS IDWOS:000515814800004
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China under Grant 11590773, Grant 61175043, and Grant 61421062 ; High-performance Computing Platform of Peking University
KeywordAnthropometric parameters HRTF individual SPCA
Abstract

Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

Language英语
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS KeywordEAR TRANSFER-FUNCTIONS
WOS Research AreaAcoustics ; Engineering
Funding ProjectNational Natural Science Foundation of China[11590773] ; National Natural Science Foundation of China[61175043] ; National Natural Science Foundation of China[61421062]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26302
Collection水下机器人研究室
Corresponding AuthorQu TS(曲天书)
Affiliation1.Key Laboratory on Machine Perception (Ministry of Education), Speech and Hearing Research Center, Peking University, Beijing, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China
Recommended Citation
GB/T 7714
Zhang, Mengfan,Ge, Zhongshu,Liu TJ,et al. Modeling of Individual HRTFs Based on Spatial Principal Component Analysis[J]. IEEE/ACM Transactions on Audio Speech and Language Processing,2020,28:785-797.
APA Zhang, Mengfan,Ge, Zhongshu,Liu TJ,Wu XH,&Qu TS.(2020).Modeling of Individual HRTFs Based on Spatial Principal Component Analysis.IEEE/ACM Transactions on Audio Speech and Language Processing,28,785-797.
MLA Zhang, Mengfan,et al."Modeling of Individual HRTFs Based on Spatial Principal Component Analysis".IEEE/ACM Transactions on Audio Speech and Language Processing 28(2020):785-797.
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