A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization
Machine learning (ML) has become pervasive in various research fields, including binaural synthesis personalization, which is crucial for sound in immersive virtual environments. Researchers have mainly addressed this topic by estimating the individual head-related transfer function (HRTF). HRTFs ar...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Signal Processing |
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| Online Access: | https://ieeexplore.ieee.org/document/10836943/ |
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| author | Davide Fantini Michele Geronazzo Federico Avanzini Stavros Ntalampiras |
| author_facet | Davide Fantini Michele Geronazzo Federico Avanzini Stavros Ntalampiras |
| author_sort | Davide Fantini |
| collection | DOAJ |
| description | Machine learning (ML) has become pervasive in various research fields, including binaural synthesis personalization, which is crucial for sound in immersive virtual environments. Researchers have mainly addressed this topic by estimating the individual head-related transfer function (HRTF). HRTFs are utilized to render audio signals at specific spatial positions, thereby simulating real-world sound wave interactions with the human body. As such, an HRTF that is compliant with individual characteristics enhances the realism of the binaural simulation. This survey systematically examines the HRTF individualization works based on ML proposed in the literature. The analyzed works are organized according to the processing steps involved in the ML workflow, including the employed dataset, input and output types, data preprocessing operations, ML models, and model evaluation. In addition to categorizing the works of the existing literature, this survey discusses their achievements, identifies their limitations, and outlines aspects that require further investigation at the crossroads of research communities in acoustics, audio signal processing, and machine learning. |
| format | Article |
| id | doaj-art-fd0c122134f94c5fb5cd1773b16d66cb |
| institution | Kabale University |
| issn | 2644-1322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj-art-fd0c122134f94c5fb5cd1773b16d66cb2025-02-04T00:00:51ZengIEEEIEEE Open Journal of Signal Processing2644-13222025-01-016305610.1109/OJSP.2025.352833010836943A Survey on Machine Learning Techniques for Head-Related Transfer Function IndividualizationDavide Fantini0https://orcid.org/0000-0003-1332-0890Michele Geronazzo1https://orcid.org/0000-0002-0621-2704Federico Avanzini2https://orcid.org/0000-0002-1257-5878Stavros Ntalampiras3https://orcid.org/0000-0003-3482-9215Laboratory of Music Informatics (LIM), Department of Computer Science, University of Milan, Milan, ItalyDepartment of Engineering and Management, University of Padua, Padova, ItalyLaboratory of Music Informatics (LIM), Department of Computer Science, University of Milan, Milan, ItalyLaboratory of Music Informatics (LIM), Department of Computer Science, University of Milan, Milan, ItalyMachine learning (ML) has become pervasive in various research fields, including binaural synthesis personalization, which is crucial for sound in immersive virtual environments. Researchers have mainly addressed this topic by estimating the individual head-related transfer function (HRTF). HRTFs are utilized to render audio signals at specific spatial positions, thereby simulating real-world sound wave interactions with the human body. As such, an HRTF that is compliant with individual characteristics enhances the realism of the binaural simulation. This survey systematically examines the HRTF individualization works based on ML proposed in the literature. The analyzed works are organized according to the processing steps involved in the ML workflow, including the employed dataset, input and output types, data preprocessing operations, ML models, and model evaluation. In addition to categorizing the works of the existing literature, this survey discusses their achievements, identifies their limitations, and outlines aspects that require further investigation at the crossroads of research communities in acoustics, audio signal processing, and machine learning.https://ieeexplore.ieee.org/document/10836943/HRTF individualizationmachine learningspatial audiobinaural synthesis |
| spellingShingle | Davide Fantini Michele Geronazzo Federico Avanzini Stavros Ntalampiras A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization IEEE Open Journal of Signal Processing HRTF individualization machine learning spatial audio binaural synthesis |
| title | A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization |
| title_full | A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization |
| title_fullStr | A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization |
| title_full_unstemmed | A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization |
| title_short | A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization |
| title_sort | survey on machine learning techniques for head related transfer function individualization |
| topic | HRTF individualization machine learning spatial audio binaural synthesis |
| url | https://ieeexplore.ieee.org/document/10836943/ |
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