{"id":"https://openalex.org/W4414360406","doi":"https://doi.org/10.24963/ijcai.2025/613","title":"A Multi-view Fusion Approach for Enhancing Speech Signals via Short-time Fractional Fourier Transform","display_name":"A Multi-view Fusion Approach for Enhancing Speech Signals via Short-time Fractional Fourier Transform","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360406","doi":"https://doi.org/10.24963/ijcai.2025/613"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/613","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/613","pdf_url":"https://www.ijcai.org/proceedings/2025/0613.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2025/0613.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048570184","display_name":"Zikun Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142037","display_name":"Shanxi University of Traditional Chinese Medicine","ror":"https://ror.org/0522dg826","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142037"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zikun Jin","raw_affiliation_strings":["Shanxi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University","institution_ids":["https://openalex.org/I4210142037"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003563112","display_name":"Yuhua Qian","orcid":"https://orcid.org/0000-0001-6772-4247"},"institutions":[{"id":"https://openalex.org/I4210142037","display_name":"Shanxi University of Traditional Chinese Medicine","ror":"https://ror.org/0522dg826","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142037"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhua Qian","raw_affiliation_strings":["Shanxi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University","institution_ids":["https://openalex.org/I4210142037"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076391595","display_name":"Xinyan Liang","orcid":"https://orcid.org/0000-0003-2589-5392"},"institutions":[{"id":"https://openalex.org/I4210142037","display_name":"Shanxi University of Traditional Chinese Medicine","ror":"https://ror.org/0522dg826","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142037"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyan Liang","raw_affiliation_strings":["Shanxi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University","institution_ids":["https://openalex.org/I4210142037"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101413343","display_name":"Haijun Geng","orcid":"https://orcid.org/0000-0002-0324-9392"},"institutions":[{"id":"https://openalex.org/I4210142037","display_name":"Shanxi University of Traditional Chinese Medicine","ror":"https://ror.org/0522dg826","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142037"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haijun Geng","raw_affiliation_strings":["Shanxi University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanxi University","institution_ids":["https://openalex.org/I4210142037"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210142037"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29274422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5508","last_page":"5516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9890000224113464,"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":0.9890000224113464,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9602000117301941,"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"}},{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9332000017166138,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.6471999883651733},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5239999890327454},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5001000165939331},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4984999895095825},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.4593000113964081},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4345000088214874},{"id":"https://openalex.org/keywords/spectrum","display_name":"Spectrum (functional analysis)","score":0.42800000309944153},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42320001125335693},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.41830000281333923}],"concepts":[{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.6471999883651733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5523999929428101},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.49889999628067017},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4668000042438507},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.4593000113964081},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C156778621","wikidata":"https://www.wikidata.org/wiki/Q1365748","display_name":"Spectrum (functional analysis)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42320001125335693},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.41830000281333923},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4041000008583069},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3684999942779541},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35040000081062317},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C8590192","wikidata":"https://www.wikidata.org/wiki/Q1054694","display_name":"Frequency response","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.2515999972820282},{"id":"https://openalex.org/C2778971668","wikidata":"https://www.wikidata.org/wiki/Q5510284","display_name":"Fusion rules","level":4,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/613","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/613","pdf_url":"https://www.ijcai.org/proceedings/2025/0613.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2025/613","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/613","pdf_url":"https://www.ijcai.org/proceedings/2025/0613.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2380867251","display_name":null,"funder_award_id":"T2495251","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5656025008","display_name":null,"funder_award_id":"202201020101006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5802660515","display_name":null,"funder_award_id":"62406218","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6702354843","display_name":null,"funder_award_id":"62472267","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7471647019","display_name":null,"funder_award_id":"62306171","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414360406.pdf","grobid_xml":"https://content.openalex.org/works/W4414360406.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"speech":[2,9,50,129],"enhancement":[3],"(SE)":[4],"methods":[5,152],"focus":[6],"on":[7,154],"reconstructing":[8],"from":[10,101],"the":[11,25,46,52,60,67,80,88,96,102,109,125,138,160,168,172],"time":[12,61,155],"or":[13],"frequency":[14,63,157],"domain.":[15],"However,":[16],"these":[17],"domains":[18,64],"cannot":[19],"provide":[20],"enough":[21],"information":[22],"to":[23,51,148],"capture":[24],"dynamics":[26],"of":[27,49,79,162,179],"non-stationary":[28],"signals":[29],"accurately.":[30],"To":[31],"enrich":[32],"information,":[33],"this":[34],"work":[35],"proposes":[36],"a":[37,176],"multi-view":[38,169],"fusion":[39],"SE":[40,151],"method":[41,140,170,174],"(MFSE).":[42],"Specifically,":[43],"MFSE":[44],"extends":[45],"representation":[47],"space":[48],"dynamic":[53],"domain":[54,106],"(also":[55],"called":[56],"fractional":[57,69,98,105],"domain)":[58],"between":[59,127],"and":[62,87,93,123,156],"by":[65,107],"using":[66],"short-time":[68,82],"Fourier":[70,83],"transform":[71,84],"(STFrFT).":[72],"Subsequently,":[73],"we":[74],"construct":[75],"inputs":[76],"as":[77],"modes":[78],"primary":[81],"(STFT)":[85],"spectrum":[86,91,100],"auxiliary":[89],"STFrFT":[90,99],"views":[92],"adaptively":[94],"identify":[95],"optimal":[97],"infinitely":[103],"continuous":[104],"leveraging":[108],"average":[110],"spectral":[111],"centroids.":[112],"The":[113],"framework":[114],"extracts":[115],"potential":[116],"features":[117],"through":[118,131],"multiple":[119],"designed":[120],"convolutional":[121],"modules":[122],"captures":[124],"correlation":[126],"different":[128],"frequencies":[130],"multi-granularity":[132],"attention.":[133],"Experimental":[134],"results":[135,161],"show":[136,166],"that":[137,167],"proposed":[139],"significantly":[141],"improves":[142],"performance":[143],"in":[144],"several":[145],"metrics":[146],"compared":[147],"existing":[149],"single-channel":[150],"based":[153],"domains.":[158],"Furthermore,":[159],"its":[163],"generalizability":[164],"evaluation":[165],"outperforms":[171],"single-view":[173],"under":[175],"wide":[177],"range":[178],"SNR":[180],"conditions.":[181]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}