{"id":"https://openalex.org/W2921427987","doi":"https://doi.org/10.23919/apsipa.2018.8659495","title":"Stacked Autoencoder Based HRTF Synthesis from Sparse Data","display_name":"Stacked Autoencoder Based HRTF Synthesis from Sparse Data","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2921427987","doi":"https://doi.org/10.23919/apsipa.2018.8659495","mag":"2921427987"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659495","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659495","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044531418","display_name":"Sunil Bharitkar","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sunil Bharitkar","raw_affiliation_strings":["HP Labs, HP Inc., Palo Alto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs, HP Inc., Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111679156","display_name":"Timothy Mauer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timothy Mauer","raw_affiliation_strings":["HP Inc., Vancouver, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Inc., Vancouver, WA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010338294","display_name":"Teresa Wells","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teresa Wells","raw_affiliation_strings":["HP Inc., Vancouver, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Inc., Vancouver, WA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085405304","display_name":"David Berfanger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Berfanger","raw_affiliation_strings":["HP Inc., Vancouver, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Inc., Vancouver, WA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3608,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56868026,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"356","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10283","display_name":"Hearing Loss and Rehabilitation","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6121647357940674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5050445199012756},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5034748911857605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5016932487487793},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4345023036003113},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.43078261613845825},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35104596614837646},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3463854193687439},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32952257990837097},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.24827799201011658}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6121647357940674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5050445199012756},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5034748911857605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5016932487487793},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4345023036003113},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.43078261613845825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35104596614837646},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3463854193687439},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32952257990837097},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.24827799201011658},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/apsipa.2018.8659495","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659495","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1535749512","https://openalex.org/W1559435080","https://openalex.org/W1581848821","https://openalex.org/W1970258520","https://openalex.org/W2005242298","https://openalex.org/W2006811939","https://openalex.org/W2019446887","https://openalex.org/W2027765653","https://openalex.org/W2027772439","https://openalex.org/W2035897331","https://openalex.org/W2037949455","https://openalex.org/W2041023324","https://openalex.org/W2046488408","https://openalex.org/W2051812123","https://openalex.org/W2053927648","https://openalex.org/W2092247469","https://openalex.org/W2096823355","https://openalex.org/W2100495367","https://openalex.org/W2105464873","https://openalex.org/W2108668360","https://openalex.org/W2126015526","https://openalex.org/W2134773428","https://openalex.org/W2140889287","https://openalex.org/W2155889930","https://openalex.org/W2166116275","https://openalex.org/W2170219111","https://openalex.org/W2334657765","https://openalex.org/W2569387811","https://openalex.org/W2592091545","https://openalex.org/W2748917647","https://openalex.org/W4235631913","https://openalex.org/W6633610931","https://openalex.org/W6674621349","https://openalex.org/W6733962499"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W4386815338","https://openalex.org/W2145836866","https://openalex.org/W2803255133"],"abstract_inverted_index":{"Ipsilateral":[0],"and":[1,49,53,177],"contralateral":[2],"head-related":[3],"transfer":[4],"functions":[5],"(HRTF),$H_{\\mathbf{ipsi}}(\\omega,":[6],"r,\\phi,\\theta)$and$H_{\\mathbf{contra}}(\\omega,":[7],"r,\\phi,\\theta)$,":[8],"are":[9,122],"used":[10],"for":[11,30,64,70,131,153],"creating":[12],"the":[13,27,71,86,95,115,132,147,151,170],"perception":[14],"of":[15,40,43,80,107,137,164],"a":[16,31,38,41,61,77,105,126,158,162],"virtual":[17],"sound":[18],"source":[19],"at":[20,67],"an":[21,112,141,154,165],"arbitrary":[22,68,142,155],"distance$r$and":[23],"azimuth-elevation":[24],"tuple$\\underline{\\psi}=[\\theta,\\phi]^{T}$relative":[25],"to":[26,47,51,140,157],"median":[28],"plane":[29],"given":[32],"frequency$\\omega$.":[33],"Publicly":[34],"available":[35],"databases":[36],"use":[37],"subset":[39],"full-grid":[42],"angular":[44],"directions":[45,69],"due":[46],"time":[48],"complexity":[50],"acquire":[52],"deconvolve":[54],"responses.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59],"present":[60],"subspace-based":[62],"technique":[63,91],"reconstructing":[65],"HRTFs":[66,81],"IRCAM-Listen":[72],"HRTF":[73,138],"database,":[74],"which":[75],"comprises":[76],"sparse":[78,96],"set":[79,106],"sampled":[82],"every":[83],"15\u00b0":[84],"along":[85],"azimuth/elevation":[87],"direction.":[88],"The":[89,118,135,167],"presented":[90],"includes":[92],"first":[93],"augmenting":[94],"IRCAM":[97],"dataset":[98],"using":[99,125],"auditory":[100],"localization":[101],"blur,":[102],"then":[103,123],"deriving":[104],"lower-dimensional":[108],"compressed":[109,148],"representation":[110],"(using":[111],"autoencoder)":[113],"from":[114,150],"augmented":[116],"HRTFs.":[117],"lower":[119],"dimensional":[120],"representations":[121],"trained":[124],"fully-connected":[127],"neural":[128],"network":[129],"(FCNN)":[130],"corresponding":[133,139],"directions.":[134],"reconstruction":[136,159],"direction$\\underline{\\psi}_{p}$is":[143],"achieved":[144],"by":[145],"applying":[146],"output":[149],"FCNN,":[152],"direction,":[156],"system":[160],"(viz.,":[161],"decoder":[163],"autoencoder).":[166],"results":[168],"demonstrate":[169],"autoencoder":[171],"approach":[172],"provides":[173],"good":[174],"quality":[175],"objective":[176],"subjective":[178],"results.":[179]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
