Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications
A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localiza...
Uloženo v:
| Vydáno v: | 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) s. 105 - 117 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.05.2022
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individual-ization that can be implemented using commercial-off-the-shelf components and performed by average users in home settings. The proposed method has two key components: a generative neural network model that can be individualized to predict HRTFs of new subjects from sparse measurements, and a lightweight measurement procedure that collects HRTF data from spatial locations. Exten-sive experiments using a public dataset and in house measurement data from 10 subjects of different ages and genders, show that the individualized models significantly outperform a baseline model in the accuracy of predicted HRTFs. To further demonstrate the advantages of individualized HRTFs, we implement two prototype applications for binaural localization and acoustic spatialization. We find that the performance of a localization model is improved by 15° after trained with individualized HRTFs. Furthermore, in hearing tests, the success rate of correctly identifying the azimuth direction of incoming sounds increases by 183% after individualization. |
|---|---|
| AbstractList | A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individual-ization that can be implemented using commercial-off-the-shelf components and performed by average users in home settings. The proposed method has two key components: a generative neural network model that can be individualized to predict HRTFs of new subjects from sparse measurements, and a lightweight measurement procedure that collects HRTF data from spatial locations. Exten-sive experiments using a public dataset and in house measurement data from 10 subjects of different ages and genders, show that the individualized models significantly outperform a baseline model in the accuracy of predicted HRTFs. To further demonstrate the advantages of individualized HRTFs, we implement two prototype applications for binaural localization and acoustic spatialization. We find that the performance of a localization model is improved by 15° after trained with individualized HRTFs. Furthermore, in hearing tests, the success rate of correctly identifying the azimuth direction of incoming sounds increases by 183% after individualization. |
| Author | Zheng, Rong El-Mohandes, Awny M. Zandi, Navid H. |
| Author_xml | – sequence: 1 givenname: Navid H. surname: Zandi fullname: Zandi, Navid H. email: hossen6@mcmaster.ca organization: McMaster University,Hamilton,Canada – sequence: 2 givenname: Awny M. surname: El-Mohandes fullname: El-Mohandes, Awny M. email: awny.elmohandes@mans.edu.eg organization: Mansoura University,Mansoura,Egypt – sequence: 3 givenname: Rong surname: Zheng fullname: Zheng, Rong email: rzheng@mcmaster.ca organization: McMaster University,Hamilton,Canada |
| BookMark | eNotjEtOwzAUAI0ECyg9ASx8gZTnv70MFaWRKkClrCvHH2QpOJGTIMHpQYXVLGY0V-g89zkgdEtgRQiYu-bl9UlwxvSKAqUrACDyDC2N0kRKwY2kXF2ifZN9-kx-tl36Tvkdb4P11T50dgoeH4rNYwwFb-bsptTnEce-4PuU7Vxsh2vXz-OUHK6HoUvOnpJrdBFtN4blPxfobfNwWG-r3fNjs653VaLApoowrgOVTChvuHGgmNXgndYxtpx46o33Kuq2dZQLEYzjGlrlQAjBvZKCLdDN3zeFEI5DSR-2fB2NphJ-7Q_t1E4H |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/IPSN54338.2022.00016 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781665496247 166549624X |
| EndPage | 117 |
| ExternalDocumentID | 9826065 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i203t-1348e26357d949c073a80dc88ffb41d2d9dd7f8bbc2455e9c480b7c05554d7653 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000855254100009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Thu Jun 29 18:36:42 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-1348e26357d949c073a80dc88ffb41d2d9dd7f8bbc2455e9c480b7c05554d7653 |
| PageCount | 13 |
| ParticipantIDs | ieee_primary_9826065 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-May |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-May |
| PublicationDecade | 2020 |
| PublicationTitle | 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) |
| PublicationTitleAbbrev | IPSN |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.8549676 |
| Snippet | A Head Related Transfer Function (HRTF) characterizes how a hu-man ear receives sounds from a point in space, and depends on the shapes of one's head, pinna,... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 105 |
| SubjectTerms | Acoustic applications Binaural Localization Conditional Variational AutoEncoder (CVAE) Data acquisition Estimation Head-Related Transfer Function (HRTF) Location awareness Neural networks Sound Spatialization Torso Transfer functions |
| Title | Individualizing Head-Related Transfer Functions for Binaural Acoustic Applications |
| URI | https://ieeexplore.ieee.org/document/9826065 |
| WOSCitedRecordID | wos000855254100009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEB1q8eBJpRW_ycGja_cj2STHKpYWpBS_6K1skgnsZSu19eCvd5KW2oMXL8sS2F2YbHiTybz3AG4q4avc8iLBUH_jhTSJ0lYk3CgvrSlcGUvZ709yPFbTqZ604HbLhUHE2HyGd-E2nuW7uV2FUllPUy5MkLkHe1KWa67Whg2Xpbo3mryMBactF-368ijDGUzMdzxTImQMDv_3sSPo_nLv2GSLKsfQwqYDz6Mtb6r-pmE2pKlJYiMbOhbxhp5lAwKp-B8xSkXZfd1UQVSD9e08enax_s5pdRfeBo-vD8Nk44aQ1HlaBM94rjBIx0inuba0NCuVOquU94ZnLnfaOemVMTbnQqC2XKVG2iDoxZ0sRXEC7Wbe4CkwSso0vaVES0vEY2UwS-nCvUPNK5GdQSfEY_axFryYbUJx_vfwBRyEgK-7AC-hvVys8Ar27dey_lxcx1n6AUWEloY |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwED2VggQToBbxjQdGAvmwa3ssiKoVJaqgoG5VbJ-lLCkqLQO_HtuNSgcWliiylEQ6x3rn8733AK4LZotU0yxCX3-jGVeRkJpFVAnLtcpMJ5Sy34c8z8VkIkcNuFlzYRAxNJ_hrb8NZ_lmppe-VHYnXS7sIHMLtr1zVs3WqvlwSSzvBqPXnFG36XL7vjQIcXob8w3XlAAavf3_fe4A2r_sOzJa48ohNLBqwctgzZwqv90w6bvJiUIrGxoSEMc9S3oOpsKfRFwySu7LqvCyGqSrZ8G1i3Q3zqvb8NZ7HD_0o9oPISrTOPOu8VSgF4_hRlKp3eIsRGy0ENYqmpjUSGO4FUrplDKGUlMRK669pBc1vMOyI2hWswqPgbi0TLq3dFC7RWKxUJjE7kKtQUkLlpxAy8dj-rGSvJjWoTj9e_gKdvvj5-F0OMifzmDPB3_VE3gOzcV8iRewo78W5ef8MszYD0tBmc8 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+21st+ACM%2FIEEE+International+Conference+on+Information+Processing+in+Sensor+Networks+%28IPSN%29&rft.atitle=Individualizing+Head-Related+Transfer+Functions+for+Binaural+Acoustic+Applications&rft.au=Zandi%2C+Navid+H.&rft.au=El-Mohandes%2C+Awny+M.&rft.au=Zheng%2C+Rong&rft.date=2022-05-01&rft.pub=IEEE&rft.spage=105&rft.epage=117&rft_id=info:doi/10.1109%2FIPSN54338.2022.00016&rft.externalDocID=9826065 |