{"id":"https://openalex.org/W4403126665","doi":"https://doi.org/10.1109/iwaenc61483.2024.10694382","title":"Weakly DOA Guided Speaker Separation with Random Look Directions and Iteratively Refined Target and Interference Priors","display_name":"Weakly DOA Guided Speaker Separation with Random Look Directions and Iteratively Refined Target and Interference Priors","publication_year":2024,"publication_date":"2024-09-09","ids":{"openalex":"https://openalex.org/W4403126665","doi":"https://doi.org/10.1109/iwaenc61483.2024.10694382"},"language":"en","primary_location":{"id":"doi:10.1109/iwaenc61483.2024.10694382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc61483.2024.10694382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://biblio.ugent.be/publication/01JA56MMG5CPZBWQHD12PEBHMA/file/01JA56QVRY60P5CF6J75DD36KB.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071309390","display_name":"Alexander Bohlender","orcid":"https://orcid.org/0000-0003-1819-0482"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Alexander Bohlender","raw_affiliation_strings":["Ghent University - Imec,IDLab,Department of Electronics and Information Systems,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University - Imec,IDLab,Department of Electronics and Information Systems,Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057432782","display_name":"Ann Spriet","orcid":"https://orcid.org/0000-0001-9331-222X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ann Spriet","raw_affiliation_strings":["Goodix Technology (Belgium) B.V.,Leuven,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goodix Technology (Belgium) B.V.,Leuven,Belgium","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018190338","display_name":"Wouter Tirry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wouter Tirry","raw_affiliation_strings":["Goodix Technology (Belgium) B.V.,Leuven,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goodix Technology (Belgium) B.V.,Leuven,Belgium","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081844255","display_name":"Nilesh Madhu","orcid":"https://orcid.org/0000-0001-9131-3309"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Nilesh Madhu","raw_affiliation_strings":["Ghent University - Imec,IDLab,Department of Electronics and Information Systems,Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ghent University - Imec,IDLab,Department of Electronics and Information Systems,Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20807358,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"80","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9988999962806702,"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":0.9988999962806702,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.7406393885612488},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.6856482028961182},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.580629825592041},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.5320870280265808},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.488830029964447},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.48703843355178833},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41800862550735474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3479011654853821},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33644241094589233},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.19409841299057007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12370812892913818},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10719719529151917}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7406393885612488},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.6856482028961182},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.580629825592041},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.5320870280265808},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.488830029964447},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.48703843355178833},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41800862550735474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3479011654853821},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33644241094589233},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.19409841299057007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12370812892913818},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10719719529151917},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iwaenc61483.2024.10694382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc61483.2024.10694382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 18th International Workshop on Acoustic Signal Enhancement (IWAENC)","raw_type":"proceedings-article"},{"id":"pmh:oai:archive.ugent.be:01JA56MMG5CPZBWQHD12PEBHMA","is_oa":true,"landing_page_url":"https://biblio.ugent.be/publication/01JA56MMG5CPZBWQHD12PEBHMA","pdf_url":"https://biblio.ugent.be/publication/01JA56MMG5CPZBWQHD12PEBHMA/file/01JA56QVRY60P5CF6J75DD36KB.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9798350361858","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:archive.ugent.be:01JA56MMG5CPZBWQHD12PEBHMA","is_oa":true,"landing_page_url":"https://biblio.ugent.be/publication/01JA56MMG5CPZBWQHD12PEBHMA","pdf_url":"https://biblio.ugent.be/publication/01JA56MMG5CPZBWQHD12PEBHMA/file/01JA56QVRY60P5CF6J75DD36KB.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISBN: 9798350361858","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4403126665.pdf"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W2030486566","https://openalex.org/W2141998673","https://openalex.org/W2403891086","https://openalex.org/W2460742184","https://openalex.org/W2734774145","https://openalex.org/W2760103357","https://openalex.org/W2891054259","https://openalex.org/W2909607850","https://openalex.org/W2972767900","https://openalex.org/W3127686677","https://openalex.org/W3160129476","https://openalex.org/W3160241658","https://openalex.org/W3171278394","https://openalex.org/W3210134537","https://openalex.org/W4225302959","https://openalex.org/W4294891356","https://openalex.org/W4310337937","https://openalex.org/W4388755868","https://openalex.org/W4389776368","https://openalex.org/W4401609536"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2077498359"],"abstract_inverted_index":{"Given":[0],"a":[1,7,25,61,98,139],"mixture":[2,39],"of":[3,28,40,51,86],"multiple":[4],"speech":[5,35,72],"signals,":[6],"neural":[8],"network":[9],"can":[10,45],"extract":[11],"the":[12,20,37,41,49,52,69,84,124,130,135],"talkers":[13],"individually":[14],"and":[15,94],"sequentially,":[16],"which":[17,117],"may":[18],"improve":[19],"output":[21],"quality":[22],"compared":[23],"to":[24,67,114],"simultaneous":[26],"separation":[27],"all":[29],"speakers.":[30],"To":[31],"still":[32],"suppress":[33],"interfering":[34],"effectively,":[36],"residual":[38],"remaining":[42],"unseparated":[43],"speakers":[44,119],"be":[46],"included":[47],"in":[48,74,116],"input":[50],"next":[53],"step.":[54],"We":[55],"build":[56],"upon":[57],"this":[58],"approach":[59],"with":[60,142],"twofold":[62],"contribution.":[63],"First,":[64],"we":[65],"propose":[66],"refine":[68],"already":[70],"extracted":[71],"signals":[73],"further":[75],"optional":[76],"iterations.":[77],"This":[78],"is":[79],"accomplished":[80],"by":[81],"exploiting":[82],"that":[83],"outputs":[85],"previous":[87],"steps":[88],"provide":[89],"prior":[90],"information":[91],"on":[92],"interference":[93,146],"target.":[95],"Experiments":[96],"indicate":[97],"gradual":[99],"improvement":[100],"until":[101],"convergence":[102],"after":[103],"about":[104],"2":[105],"iterations":[106],"per":[107],"speaker.":[108],"Secondly,":[109],"look":[110],"directions":[111,144],"are":[112,120],"defined":[113],"control":[115],"order":[118],"extracted,":[121],"thereby":[122],"resolving":[123],"related":[125],"permutation":[126],"ambiguity.":[127],"Whereas":[128],"supplying":[129],"true":[131],"speaker":[132],"locations":[133],"delivers":[134],"best":[136],"results,":[137],"even":[138],"weak":[140],"guidance":[141],"random":[143],"reduces":[145],"leakage":[147],"significantly.":[148]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}