Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition
Head-related transfer function (HRTF) plays an important role in three-dimensional spatial sound system. However, the direct application of a large amount of original HRTF data would involve a great deal of computational burden, especially for high-spatial-resolution individual HRTF. To address this...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8672134/ |
| _version_ | 1819078804630405120 |
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| author | Jing Wang Min Liu Xiang Xie Jingming Kuang |
| author_facet | Jing Wang Min Liu Xiang Xie Jingming Kuang |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Head-related transfer function (HRTF) plays an important role in three-dimensional spatial sound system. However, the direct application of a large amount of original HRTF data would involve a great deal of computational burden, especially for high-spatial-resolution individual HRTF. To address this problem, we propose a novel compression method (called TT-Tucker) combining Tucker model with tensor train decomposition based on a 5-order HRTF tensor model developed in subspaces of an ear, subject, azimuth, elevation, and frequency. Lots of HRTF data can be decomposed into several low-parametric factors representing the key spectrum information of HRTF by capturing the hidden interactions among different subspaces. To evaluate the reconstruction performance, the numerical experiments were conducted on the CIPIC HRTF database. Under the same compression ratio of nearly 98%, the results suggest that the proposed method has a better performance in spectral distortion and signal-to-distortion ratio than that of the usual tensor method and the standard method principal component analysis (PCA). Moreover, the subjective listening test shows that the TT-Tucker method performs better in that, the compressed and reconstructed HRTF is closer to the original HRTF in the sound localization similarity. |
| first_indexed | 2024-12-21T19:18:55Z |
| format | Article |
| id | doaj.art-c7027a9b9e494f688db08744eae23d89 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T19:18:55Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c7027a9b9e494f688db08744eae23d892022-12-21T18:53:00ZengIEEEIEEE Access2169-35362019-01-017396393965110.1109/ACCESS.2019.29063648672134Compression of Head-Related Transfer Function Based on Tucker and Tensor Train DecompositionJing Wang0https://orcid.org/0000-0002-3653-9951Min Liu1Xiang Xie2Jingming Kuang3School of Information and Electronics, Beijing Institute of Technology University, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology University, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology University, Beijing, ChinaSchool of Information and Electronics, Beijing Institute of Technology University, Beijing, ChinaHead-related transfer function (HRTF) plays an important role in three-dimensional spatial sound system. However, the direct application of a large amount of original HRTF data would involve a great deal of computational burden, especially for high-spatial-resolution individual HRTF. To address this problem, we propose a novel compression method (called TT-Tucker) combining Tucker model with tensor train decomposition based on a 5-order HRTF tensor model developed in subspaces of an ear, subject, azimuth, elevation, and frequency. Lots of HRTF data can be decomposed into several low-parametric factors representing the key spectrum information of HRTF by capturing the hidden interactions among different subspaces. To evaluate the reconstruction performance, the numerical experiments were conducted on the CIPIC HRTF database. Under the same compression ratio of nearly 98%, the results suggest that the proposed method has a better performance in spectral distortion and signal-to-distortion ratio than that of the usual tensor method and the standard method principal component analysis (PCA). Moreover, the subjective listening test shows that the TT-Tucker method performs better in that, the compressed and reconstructed HRTF is closer to the original HRTF in the sound localization similarity.https://ieeexplore.ieee.org/document/8672134/Head-related transfer functioncompressiontensor trainTucker model |
| spellingShingle | Jing Wang Min Liu Xiang Xie Jingming Kuang Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition IEEE Access Head-related transfer function compression tensor train Tucker model |
| title | Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition |
| title_full | Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition |
| title_fullStr | Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition |
| title_full_unstemmed | Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition |
| title_short | Compression of Head-Related Transfer Function Based on Tucker and Tensor Train Decomposition |
| title_sort | compression of head related transfer function based on tucker and tensor train decomposition |
| topic | Head-related transfer function compression tensor train Tucker model |
| url | https://ieeexplore.ieee.org/document/8672134/ |
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