{"id":"https://openalex.org/W4392904656","doi":"https://doi.org/10.1109/icassp48485.2024.10447867","title":"Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition","display_name":"Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904656","doi":"https://doi.org/10.1109/icassp48485.2024.10447867"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5025076683","display_name":"Ying Peng","orcid":"https://orcid.org/0000-0003-2419-0702"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Peng","raw_affiliation_strings":["Tongji University,China","Tongji University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Tongji University, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018401644","display_name":"Yihong Dong","orcid":"https://orcid.org/0000-0001-6228-4019"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihong Dong","raw_affiliation_strings":["Peking University,China","Peking University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University,China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012381843","display_name":"Muqiao Yang","orcid":"https://orcid.org/0000-0001-6273-0138"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muqiao Yang","raw_affiliation_strings":["Carnegie Mellon University,USA","Carnegie Mellon University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"Carnegie Mellon University, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088593720","display_name":"Songtao Lu","orcid":"https://orcid.org/0000-0001-9256-9648"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Songtao Lu","raw_affiliation_strings":["IBM Thomas J. Watson Research Center,USA","IBM Thomas J. Watson Research Center, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center,USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059252324","display_name":"Qingjiang Shi","orcid":"https://orcid.org/0000-0003-0507-9080"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingjiang Shi","raw_affiliation_strings":["Tongji University,China","Tongji University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tongji University,China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Tongji University, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5445","last_page":"5449"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9977999925613403,"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"}},"topics":[{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9977999925613403,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9746999740600586,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9735000133514404,"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/computer-science","display_name":"Computer science","score":0.7530347108840942},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6508901715278625},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5205832123756409},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5178582668304443},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5144689083099365},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5068984627723694},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5014829635620117},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49339738488197327},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.46199822425842285},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.4421325922012329},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.42395055294036865},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41929659247398376},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12008938193321228},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08185446262359619}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7530347108840942},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6508901715278625},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5205832123756409},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5178582668304443},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5144689083099365},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5068984627723694},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5014829635620117},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49339738488197327},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.46199822425842285},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.4421325922012329},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.42395055294036865},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41929659247398376},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12008938193321228},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08185446262359619},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447867","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447867","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W142384176","https://openalex.org/W1522301498","https://openalex.org/W1836465849","https://openalex.org/W2058399474","https://openalex.org/W2124348301","https://openalex.org/W2134563598","https://openalex.org/W2272847350","https://openalex.org/W2604763608","https://openalex.org/W2787501667","https://openalex.org/W2896457183","https://openalex.org/W3011574394","https://openalex.org/W3015651375","https://openalex.org/W3016147814","https://openalex.org/W3016245588","https://openalex.org/W3038716110","https://openalex.org/W3094286125","https://openalex.org/W3118059217","https://openalex.org/W3138656191","https://openalex.org/W3160820458","https://openalex.org/W3179953624","https://openalex.org/W4294646197","https://openalex.org/W4372349118","https://openalex.org/W4385245566","https://openalex.org/W4394666973"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W3048601286","https://openalex.org/W2965925734","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Deep":[0],"neural":[1,51],"networks":[2],"have":[3],"been":[4],"shown":[5],"as":[6],"a":[7,118],"class":[8],"of":[9,27,32,43,49,58,89,103,142],"useful":[10],"tools":[11],"for":[12,21,75,124],"addressing":[13],"signal":[14,126],"recognition":[15,127],"issues":[16],"in":[17,69,112,128],"recent":[18],"years,":[19],"especially":[20],"identifying":[22],"the":[23,47,56,70,86,90,100,104,129,140,143,148],"nonlinear":[24],"feature":[25],"structures":[26],"signals.":[28,94],"However,":[29],"this":[30,96],"power":[31],"most":[33],"deep":[34],"learning":[35],"techniques":[36],"heavily":[37],"relies":[38],"on":[39],"an":[40,76],"abundant":[41],"amount":[42],"training":[44,59],"data,":[45],"so":[46],"performance":[48],"classic":[50],"nets":[52],"decreases":[53],"sharply":[54],"when":[55],"number":[57],"data":[60,66,91],"samples":[61,92],"is":[62],"small":[63],"or":[64,93],"unseen":[65],"are":[67],"presented":[68],"testing":[71],"phase.":[72],"This":[73],"calls":[74],"advanced":[77],"strategy,":[78],"i.e.,":[79,106],"model-agnostic":[80],"meta-learning":[81],"(MAML),":[82],"which":[83],"can":[84],"capture":[85],"invariant":[87],"representation":[88],"In":[95],"paper,":[97],"inspired":[98],"by":[99,132],"special":[101],"structure":[102],"signal,":[105],"real":[107],"and":[108,135],"imaginary":[109],"parts":[110],"consisted":[111],"practical":[113],"time-series":[114],"signals,":[115],"we":[116],"propose":[117],"Complex-valued":[119],"Attentional":[120],"MEta":[121],"Learner":[122],"(CAMEL)":[123],"few-shot":[125],"complex":[130],"domain":[131],"leveraging":[133],"attention":[134],"meta-learning.":[136],"Experimental":[137],"results":[138],"showcase":[139],"superiority":[141],"proposed":[144],"CAMEL":[145],"compared":[146],"with":[147],"state-of-the-art":[149],"methods.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}