{"id":"https://openalex.org/W7165751871","doi":"https://doi.org/10.48550/arxiv.2606.23761","title":"Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks","display_name":"Neuromorphic Speech Enhancement with Dual-Branch Spiking Neural Networks","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165751871","doi":"https://doi.org/10.48550/arxiv.2606.23761"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23761","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139245803","display_name":"Taiyu Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Taiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139224971","display_name":"Wenbin Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Wenbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028855371","display_name":"Haoyi Zhang","orcid":"https://orcid.org/0009-0009-3693-786X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139275310","display_name":"Yuhan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yuhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049284356","display_name":"Haibing Yin","orcid":"https://orcid.org/0000-0002-3025-0938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Haibing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.7728000283241272,"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":0.7728000283241272,"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":0.10220000147819519,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.030899999663233757,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.8026000261306763},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7868000268936157},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6190999746322632},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6051999926567078},{"id":"https://openalex.org/keywords/pesq","display_name":"PESQ","score":0.6021000146865845},{"id":"https://openalex.org/keywords/speech-enhancement","display_name":"Speech enhancement","score":0.5827999711036682},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5101000070571899},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49380001425743103}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.8026000261306763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896000146865845},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7868000268936157},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6190999746322632},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C103734657","wikidata":"https://www.wikidata.org/wiki/Q2739975","display_name":"PESQ","level":4,"score":0.6021000146865845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5952000021934509},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.5827999711036682},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5101000070571899},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36559998989105225},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3093000054359436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C33010914","wikidata":"https://www.wikidata.org/wiki/Q7291980","display_name":"Random neural network","level":4,"score":0.29910001158714294},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2660999894142151}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23761","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23761","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.901172935962677}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Spiking":[0],"neural":[1,24,49],"network":[2,25,37,50],"(SNN)-based":[3],"neuromorphic":[4],"speech":[5,66],"enhancement":[6],"has":[7],"emerged":[8],"as":[9],"a":[10,45,54,81,100,108],"promising":[11],"paradigm":[12],"due":[13],"to":[14,29,87],"its":[15],"energy":[16],"efficiency,":[17],"yet":[18],"it":[19],"still":[20],"underperforms":[21],"classical":[22],"artificial":[23],"(ANN)-based":[26],"approaches":[27],"owing":[28],"binary":[30],"activations":[31],"and":[32,69,76,90],"the":[33,65,73,126],"lack":[34],"of":[35,111,125,128],"well-designed":[36],"architectures.":[38],"To":[39],"overcome":[40],"this":[41],"limitation,":[42],"we":[43],"propose":[44],"novel":[46],"dual-branch":[47],"spiking":[48,56],"architecture":[51],"equipped":[52],"with":[53,113],"gated":[55],"unit":[57],"(GSU),":[58],"termed":[59],"GSU-DBNet.":[60],"Specifically,":[61],"GSU-DBNet":[62,106],"simultaneously":[63],"models":[64],"magnitude":[67,75],"spectrum":[68],"complex":[70,77],"spectrum,":[71],"predicting":[72],"corresponding":[74],"spectral":[78],"masks.":[79],"Meanwhile,":[80],"dual-path":[82],"GSU":[83],"module":[84],"is":[85],"adopted":[86],"exploit":[88],"temporal":[89],"frequency":[91],"information":[92],"for":[93],"enhanced":[94],"spatiotemporal":[95],"feature":[96],"representation.":[97],"Experiments":[98],"on":[99],"popular":[101],"benchmark":[102],"dataset":[103],"show":[104],"that":[105],"achieves":[107],"PESQ":[109],"score":[110],"3.04":[112],"only":[114,123],"394K":[115],"parameters,":[116],"outperforming":[117],"existing":[118],"SNN-based":[119],"methods":[120],"while":[121],"using":[122],"4.5%--10.6%":[124],"parameters":[127],"representative":[129],"ANN-based":[130],"models.":[131]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-25T00:00:00"}