Modeling of Individual HRTFs Based on Spatial Principal Component Analysis | |
Zhang, Mengfan1; Ge, Zhongshu1; Liu TJ(刘铁军)2![]() | |
Department | 水下机器人研究室 |
Source Publication | IEEE/ACM Transactions on Audio Speech and Language Processing
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ISSN | 2329-9290 |
2020 | |
Volume | 28Pages:785-797 |
Indexed By | SCI ; EI |
EI Accession number | 20200708179413 |
WOS ID | WOS:000515814800004 |
Contribution Rank | 2 |
Funding Organization | National Natural Science Foundation of China under Grant 11590773, Grant 61175043, and Grant 61421062 ; High-performance Computing Platform of Peking University |
Keyword | Anthropometric 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 Subject | Acoustics ; Engineering, Electrical & Electronic |
WOS Keyword | EAR TRANSFER-FUNCTIONS |
WOS Research Area | Acoustics ; Engineering |
Funding Project | National 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 |
Identifier | http://ir.sia.cn/handle/173321/26302 |
Collection | 水下机器人研究室 |
Corresponding Author | Qu TS(曲天书) |
Affiliation | 1.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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File Name/Size | DocType | Version | Access | License | ||
Modeling of Individu(3364KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text |
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