Head-Related Transfer Function Selection Using Neural Networks
In binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listen...
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| Format: | Article |
| Language: | English |
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Institute of Fundamental Technological Research Polish Academy of Sciences
2017-07-01
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| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1841 |
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| _version_ | 1849415975772880896 |
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| author | Shu-Nung YAO Tim COLLINS Chaoyun LIANG |
| author_facet | Shu-Nung YAO Tim COLLINS Chaoyun LIANG |
| author_sort | Shu-Nung YAO |
| collection | DOAJ |
| description | In binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listening
tests. Currently, there is no way to reliably reduce the number of datasets to a smaller, more manageable number without risking discarding potentially good matches. A neural network that estimates the appropriateness of HRTF datasets based on input vectors of anthropometric measurements is proposed. The shapes and sizes of listeners’ heads and pinnas were measured using digital photography; the measured anthropometric parameters form the feature vectors used by the neural network. A graphical user interface (GUI) was developed for participants to listen to music transformed using different HRTFs and
to evaluate the fitness of each HRTF dataset. The listening scores recorded were the target outputs used to train the neural networks. The aim was to learn a mapping between anthropometric parameters and listener’s perception scores. Experimental validations were performed on 30 subjects. It is demonstrated that the proposed system produces a much more reliable HRTF selection than previously used methods. |
| format | Article |
| id | doaj-art-3f9da78bb5394f0da16e88b267e6a40f |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2017-07-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-3f9da78bb5394f0da16e88b267e6a40f2025-08-20T03:33:19ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2017-07-0142310.1515/aoa-2017-0038Head-Related Transfer Function Selection Using Neural NetworksShu-Nung YAO0Tim COLLINS1Chaoyun LIANG2National Taipei UniversityManchester Metropolitan UniversityNational Taiwan UniversityIn binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listening tests. Currently, there is no way to reliably reduce the number of datasets to a smaller, more manageable number without risking discarding potentially good matches. A neural network that estimates the appropriateness of HRTF datasets based on input vectors of anthropometric measurements is proposed. The shapes and sizes of listeners’ heads and pinnas were measured using digital photography; the measured anthropometric parameters form the feature vectors used by the neural network. A graphical user interface (GUI) was developed for participants to listen to music transformed using different HRTFs and to evaluate the fitness of each HRTF dataset. The listening scores recorded were the target outputs used to train the neural networks. The aim was to learn a mapping between anthropometric parameters and listener’s perception scores. Experimental validations were performed on 30 subjects. It is demonstrated that the proposed system produces a much more reliable HRTF selection than previously used methods.https://acoustics.ippt.pan.pl/index.php/aa/article/view/1841head-related transfer functionneural networkslocalizationmusicaudioanthropometry |
| spellingShingle | Shu-Nung YAO Tim COLLINS Chaoyun LIANG Head-Related Transfer Function Selection Using Neural Networks Archives of Acoustics head-related transfer function neural networks localization music audio anthropometry |
| title | Head-Related Transfer Function Selection Using Neural Networks |
| title_full | Head-Related Transfer Function Selection Using Neural Networks |
| title_fullStr | Head-Related Transfer Function Selection Using Neural Networks |
| title_full_unstemmed | Head-Related Transfer Function Selection Using Neural Networks |
| title_short | Head-Related Transfer Function Selection Using Neural Networks |
| title_sort | head related transfer function selection using neural networks |
| topic | head-related transfer function neural networks localization music audio anthropometry |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/1841 |
| work_keys_str_mv | AT shunungyao headrelatedtransferfunctionselectionusingneuralnetworks AT timcollins headrelatedtransferfunctionselectionusingneuralnetworks AT chaoyunliang headrelatedtransferfunctionselectionusingneuralnetworks |