[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121427-en":3,"doc-seo-121427-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121427,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization - Machine Learning Survey","Machine learning has become essential to binaural synthesis personalization for realistic audio in immersive virtual environments. This survey systematically reviews machine-learning approaches to head-related transfer function (HRTF) individualization. It organizes prior work by the processing steps of the ML workflow, covering datasets, input/output representations, preprocessing, model choices, and evaluation strategies. Beyond classification, it summarizes reported achievements, highlights limitations, and identifies open directions requiring further study across acoustics, audio signal processing, and machine learning research communities.","Received 5 August 2024; revised 11 November 2024; accepted 4 December 2024. Date of publication 10 January 2025; date of current version 4 February 2025. The review of this article was arranged by Associate Editor Romain Serizel.  \nDigital Object Identifier 10.1109/OJSP.2025.3528330  \nA Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization  \nDAVIDE FANTINI , MICHELE GERONAZZO  (Senior Member, IEEE), FEDERICO AVANZINI  ,  \nAND STAVROS NTALAMPIRAS   \n1Laboratory of Music Informatics (LIM), Department of Computer Science, University of Milan, 20133 Milan, Italy  \n2Department of Engineering and Management, University of Padua, 35122 Padova, Italy  \n3Dyson School of Design Engineering, Imperial College London, SW7 2AZ London, U.K.  \nCORRESPONDING AUTHOR: DAVIDE FANTINI (email: [davide.fantini@unimi.it](davide.fantini@unimi.it)).  \nThis work was supported by SONICOM, through European Union’s Horizon 2020 Research and Innovation Programme under Grant 101017743 .  \nThis article has supplementary downloadable material available at [https://doi.org/10.1109/OJSP.2025.3528330](https://doi.org/10.1109/OJSP.2025.3528330), provided by the authors.  \nABSTRACT 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.  \nINDEX TERMS HRTF individualization, machine learning, spatial audio, binaural synthesis.  \nI. INTRODUCTION  \nMachine learning (ML) can be defined as the learning of algorithms to solve a specific problem based on information extracted from previous experiences or events, rather than explicitly programming the algorithm [1] . ML has become pervasive in several aspects of society over the past few years, with both industrial and scientific applications. The field of spatial audio is no exception. Spatial audio techniques find several applications, including video gaming, teleconferencing, art, flight simulation [2], devices for blind people [3], and audio production [4] . An appropriate spatial audio simulation involves the simulation of the spatial cues used by humans to localize sound sources in space. These spatial cues originate from the interactions between the human body and  \nthe sound waves, which result in position-dependent sound alterations. Head-related transfer functions (HRTFs) describe these spatial cues as a linear time-invariant (LTI) system for each sound source position of interest and for each ear. The use of an HRTF of a specific position to spatialize an audio signal spatialized with an HRTF of a specific position through headphones artificially creates the sensation of a sound source in that position. HRTFs are individual due to their close relationship with anatomical traits. Therefore, the use of an HRTF non-compliant with the individual anatomy, i.e., a non-individual HRTF, results in an improper spatial audio experience [5], [6], [7], [8], [9], [10], [11], [12] . Nonindividual HRTF","cbCaihzkOfs5FkWF","https://ap.wps.com/l/cbCaihzkOfs5FkWF","pdf",2707064,1,27,"English","en",105,"# Introduction\n## Spatial audio and HRTFs\n## Motivation for HRTF individualization\n# Survey organization of ML-based methods","[{\"question\":\"What problem does the survey address?\",\"answer\":\"The survey addresses ML-based head-related transfer function (HRTF) individualization, aiming to estimate individual-compliant HRTFs for improved binaural and spatial audio realism.\"},{\"question\":\"How does the survey organize the reviewed works?\",\"answer\":\"The reviewed ML works are organized according to the ML workflow processing steps, including datasets, input/output types, preprocessing, model architectures, and model evaluation.\"},{\"question\":\"Why is standardization difficult in HRTF individualization research?\",\"answer\":\"The document notes that methods are often proposed with no common validation procedure, lacking consistent datasets, objective metrics, and rigorous perceptual tests, which makes comparisons difficult.\"}]","A Survey on Machine Learning Techniques for Head-Related Transfer Function Individualization - Machine Learning Survey | PDF",1785735615,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-survey-on-machine-learning-techniques-for-head-related-transfer-function-individualization-machine-learning-survey","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-survey-on-machine-learning-techniques-for-head-related-transfer-function-individualization-machine-learning-survey/121427/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the survey address?","Question",{"text":75,"@type":76},"The survey addresses ML-based head-related transfer function (HRTF) individualization, aiming to estimate individual-compliant HRTFs for improved binaural and spatial audio realism.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the survey organize the reviewed works?",{"text":80,"@type":76},"The reviewed ML works are organized according to the ML workflow processing steps, including datasets, input/output types, preprocessing, model architectures, and model evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is standardization difficult in HRTF individualization research?",{"text":84,"@type":76},"The document notes that methods are often proposed with no common validation procedure, lacking consistent datasets, objective metrics, and rigorous perceptual tests, which makes comparisons difficult.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]