{"id":"https://openalex.org/W2149780870","doi":"https://doi.org/10.1109/ijcnn.2008.4633971","title":"Nonlinear Hebbian Learning for noise-independent vehicle sound recognition","display_name":"Nonlinear Hebbian Learning for noise-independent vehicle sound recognition","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2149780870","doi":"https://doi.org/10.1109/ijcnn.2008.4633971","mag":"2149780870"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4633971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},"type":"article","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/A5100339966","display_name":"Bing Lu","orcid":"https://orcid.org/0009-0007-7633-0486"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bing Lu","raw_affiliation_strings":["Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055411556","display_name":"Alireza A. Dibazar","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alireza Dibazar","raw_affiliation_strings":["Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111911478","display_name":"Theodore W. Berger","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Theodore W. Berger","raw_affiliation_strings":["Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100339966"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":1.493,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83845438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1336","last_page":"1343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9995999932289124,"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":0.9995999932289124,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9991999864578247,"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/T11309","display_name":"Music and Audio Processing","score":0.9991000294685364,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hebbian-theory","display_name":"Hebbian theory","score":0.7731318473815918},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6570644974708557},{"id":"https://openalex.org/keywords/learning-rule","display_name":"Learning rule","score":0.5337420701980591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5162772536277771},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4830354154109955},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4819492995738983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4658789038658142},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4474336504936218},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4451078772544861},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44268685579299927},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43459486961364746},{"id":"https://openalex.org/keywords/competitive-learning","display_name":"Competitive learning","score":0.4126850366592407}],"concepts":[{"id":"https://openalex.org/C111437709","wikidata":"https://www.wikidata.org/wiki/Q1277874","display_name":"Hebbian theory","level":3,"score":0.7731318473815918},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6570644974708557},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.5337420701980591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5162772536277771},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4830354154109955},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4819492995738983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4658789038658142},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4474336504936218},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4451078772544861},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44268685579299927},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43459486961364746},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.4126850366592407},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2008.4633971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W17865367","https://openalex.org/W58363153","https://openalex.org/W80657276","https://openalex.org/W396690109","https://openalex.org/W1489184006","https://openalex.org/W1546472312","https://openalex.org/W1580570254","https://openalex.org/W1766888123","https://openalex.org/W1940630837","https://openalex.org/W1990938546","https://openalex.org/W1991129840","https://openalex.org/W2046739774","https://openalex.org/W2050758723","https://openalex.org/W2056612043","https://openalex.org/W2059148040","https://openalex.org/W2063853231","https://openalex.org/W2068346853","https://openalex.org/W2092703677","https://openalex.org/W2106939211","https://openalex.org/W2108384452","https://openalex.org/W2112575974","https://openalex.org/W2112927743","https://openalex.org/W2114187834","https://openalex.org/W2130476329","https://openalex.org/W2131329059","https://openalex.org/W2144331580","https://openalex.org/W2145624369","https://openalex.org/W2156370188","https://openalex.org/W2432567885","https://openalex.org/W2478884216","https://openalex.org/W2994602346","https://openalex.org/W4230432527","https://openalex.org/W6613520308","https://openalex.org/W6634760443","https://openalex.org/W6677259823","https://openalex.org/W6986976741"],"related_works":["https://openalex.org/W2590565095","https://openalex.org/W2896927215","https://openalex.org/W1950019275","https://openalex.org/W4387853727","https://openalex.org/W3180828476","https://openalex.org/W2123887195","https://openalex.org/W2963219950","https://openalex.org/W4285340753","https://openalex.org/W1520894008","https://openalex.org/W1530541224"],"abstract_inverted_index":{"In":[0],"this":[1,141,256,260],"paper":[2],"we":[3],"propose":[4],"using":[5,39],"a":[6,9,56,71,168],"new":[7],"approach,":[8],"nonlinear":[10,72,119,134,151],"Hebbian":[11,73,135],"learning,":[12,98,153],"to":[13,54,79,89,132,167,222,268],"implement":[14],"acoustic":[15,64],"signature":[16,65],"recognition":[17],"of":[18,31,66,93,146,162],"running":[19,67],"vehicles.":[20],"The":[21,34,43,189],"proposed":[22,131,190,211],"learning":[23,74],"rule":[24,76],"processes":[25],"both":[26,176],"time":[27],"and":[28,88,103,154,178,206,235],"frequency":[29],"components":[30],"input":[32,102,187,274],"data.":[33,188],"spectral":[35],"analysis":[36],"is":[37,77,130,138],"realized":[38],"auditory":[40],"gammatone":[41],"filterbanks.":[42],"gammatone-filtered":[44],"feature":[45,95],"vectors":[46],"are":[47,106],"then":[48],"assembled":[49],"over":[50],"multiple":[51,125],"temporal":[52],"frames":[53],"establish":[55],"high-dimensional":[57,273],"spectro-temporal":[58,86],"representation":[59],"(STR).":[60],"With":[61],"the":[62,85,91,94,115,144,147,159,210,219],"exact":[63],"vehicles":[68],"being":[69],"unknown,":[70],"(NHL)":[75],"employed":[78],"extract":[80,182],"representative":[81],"independent":[82,126],"features":[83,184,271],"from":[84,185,272],"ones":[87],"reduce":[90],"dimensionality":[92],"space.":[96],"During":[97],"synaptic":[99,112],"weights":[100],"between":[101],"output":[104],"neurons":[105],"adaptively":[107],"learned.":[108],"Motivated":[109],"by":[110],"neurobiological":[111],"transmission":[113],"in":[114,150],"brain,":[116],"one":[117],"specific":[118],"activation":[120,148],"function,":[121],"which":[122],"can":[123,180,217,236],"represent":[124],"neural":[127,152],"signaling":[128],"pathways,":[129],"process":[133],"learning.":[136,171],"It":[137,216],"shown":[139],"that":[140,155,175],"function":[142,149,237],"satisfies":[143],"requirements":[145],"its":[156,199,250],"derivative":[157],"matches":[158],"implicit":[160],"distribution":[161],"vehicle":[163],"sounds,":[164],"thus":[165],"leading":[166],"statistically":[169],"optimal":[170],"Simulation":[172],"results":[173],"show":[174],"STR":[177],"NHL":[179],"accurately":[181],"critical":[183],"original":[186],"model":[191,212],"achieves":[192],"better":[193],"performance":[194],"under":[195],"noisy":[196],"environments":[197],"than":[198],"counterparts.":[200],"For":[201],"additive":[202],"white":[203],"Gaussian":[204],"noise":[205],"common":[207],"colored":[208],"noise,":[209],"demonstrates":[213],"excellent":[214],"robustness.":[215],"decrease":[218],"error":[220,240],"rate":[221,241],"3%":[223],"with":[224,239],"improvement":[225],"21":[226],"~":[227,243],"34%":[228],"at":[229,245,255],"signal-to-noise":[230],"ratio":[231],"(SNR)=":[232],"0":[233],"dB,":[234],"efficiently":[238],"7":[242],"8%":[244],"low":[246],"SNR=-6":[247],"dB":[248],"when":[249],"counterparts":[251],"cannot":[252],"work":[253],"properly":[254],"situation.":[257],"To":[258],"summarize,":[259],"study":[261],"not":[262],"only":[263],"provides":[264],"an":[265],"efficient":[266],"way":[267],"capture":[269],"important":[270],"signals":[275],"but":[276],"also":[277],"offers":[278],"robustness":[279],"against":[280],"severe":[281],"background":[282],"noise.":[283]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}