{"id":"https://openalex.org/W2972612102","doi":"https://doi.org/10.21437/interspeech.2019-1860","title":"Sound Event Detection in Multichannel Audio Using Convolutional Time-Frequency-Channel Squeeze and Excitation","display_name":"Sound Event Detection in Multichannel Audio Using Convolutional Time-Frequency-Channel Squeeze and Excitation","publication_year":2019,"publication_date":"2019-09-13","ids":{"openalex":"https://openalex.org/W2972612102","doi":"https://doi.org/10.21437/interspeech.2019-1860","mag":"2972612102"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2019-1860","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2019-1860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2019","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/A5101861591","display_name":"Wei Xia","orcid":"https://orcid.org/0009-0009-4734-6256"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Xia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084879161","display_name":"Kazuhito Koishida","orcid":"https://orcid.org/0000-0002-3111-5375"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kazuhito Koishida","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":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3629","last_page":"3633"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9997000098228455,"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/T11309","display_name":"Music and Audio Processing","score":0.9997000098228455,"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/T10860","display_name":"Speech and Audio Processing","score":0.9994999766349792,"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/T11349","display_name":"Music Technology and Sound Studies","score":0.9866999983787537,"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/computer-science","display_name":"Computer science","score":0.769508957862854},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6261906623840332},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6150507926940918},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6085036396980286},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5524906516075134},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5489200353622437},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.5425790548324585},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5410385131835938},{"id":"https://openalex.org/keywords/microphone","display_name":"Microphone","score":0.4421733021736145},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43398991227149963},{"id":"https://openalex.org/keywords/microphone-array","display_name":"Microphone array","score":0.4305742681026459},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4289812445640564},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.42321357131004333},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3656834065914154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3341634273529053},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13809219002723694},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11970636248588562},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10674351453781128}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.769508957862854},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6261906623840332},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6150507926940918},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6085036396980286},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5524906516075134},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5489200353622437},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.5425790548324585},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5410385131835938},{"id":"https://openalex.org/C2778263558","wikidata":"https://www.wikidata.org/wiki/Q46384","display_name":"Microphone","level":3,"score":0.4421733021736145},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43398991227149963},{"id":"https://openalex.org/C2778806681","wikidata":"https://www.wikidata.org/wiki/Q907293","display_name":"Microphone array","level":4,"score":0.4305742681026459},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4289812445640564},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.42321357131004333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3656834065914154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3341634273529053},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13809219002723694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11970636248588562},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10674351453781128},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C68115822","wikidata":"https://www.wikidata.org/wiki/Q1068172","display_name":"Sound pressure","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2019-1860","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2019-1860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2019","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1879255185","https://openalex.org/W2120442551","https://openalex.org/W2769861442","https://openalex.org/W1980506188","https://openalex.org/W2900122540","https://openalex.org/W4240587264","https://openalex.org/W2782295999","https://openalex.org/W2043735059","https://openalex.org/W2041060376","https://openalex.org/W2162306796"],"abstract_inverted_index":{"In":[0],"this":[1],"study,":[2],"we":[3],"introduce":[4],"a":[5,61,72,112],"convolutional":[6],"time-frequency-channel":[7],"Squeeze":[8],"and":[9,21,33,44,84,117,166,187],"Excitation":[10],"(tfc-SE)":[11],"module":[12,26,103,183],"to":[13,39,159,191],"explicitly":[14],"model":[15,110],"inter-dependencies":[16],"between":[17],"the":[18,51,82,101,108,139,142,149,176,181,193],"time-frequency":[19,43,186],"domain":[20],"multiple":[22],"channels.":[23],"The":[24,55,172],"tfc-SE":[25,57,102,151,182],"consists":[27],"of":[28,141,164,169],"two":[29],"parts:":[30],"tf-SE":[31],"block":[32,35],"c-SE":[34],"which":[36],"are":[37,69,87],"designed":[38],"provide":[40],"attention":[41],"on":[42,71,121],"channel":[45],"domain,":[46],"respectively,":[47],"for":[48],"adaptively":[49],"recalibrating":[50],"input":[52],"feature":[53,189],"map.":[54],"proposed":[56],"module,":[58],"together":[59],"with":[60,78,95,148,180],"popular":[62],"Convolutional":[63],"Recurrent":[64],"Neural":[65],"Network":[66],"(CRNN)":[67],"model,":[68],"evaluated":[70],"multi-channel":[73],"sound":[74,122],"event":[75,123],"detection":[76,124],"task":[77],"overlapping":[79],"audio":[80],"sources:":[81],"training":[83],"test":[85],"data":[86],"synthesized":[88],"TUT":[89],"Sound":[90],"Events":[91],"2018":[92],"datasets,":[93],"recorded":[94],"microphone":[96],"arrays.":[97],"We":[98,126,145],"show":[99,146],"that":[100,136,147,175],"can":[104],"be":[105],"incorporated":[106],"into":[107],"CRNN":[109],"at":[111],"small":[113],"additional":[114],"computational":[115],"cost":[116],"bring":[118],"significant":[119],"improvements":[120],"accuracy.":[125],"also":[127],"perform":[128],"detailed":[129],"ablation":[130],"studies":[131],"by":[132],"analyzing":[133],"various":[134],"factors":[135],"may":[137],"influence":[138],"performance":[140],"SE":[143],"blocks.":[144],"best":[150],"block,":[152],"error":[153],"rate":[154],"(ER)":[155],"decreases":[156],"from":[157],"0.2538":[158],"0.2026,":[160],"relative":[161],"20.17\\%":[162],"reduction":[163],"ER,":[165],"5.72\\%":[167],"improvement":[168],"F1":[170],"score.":[171],"results":[173],"indicate":[174],"learned":[177],"acoustic":[178],"embeddings":[179],"efficiently":[184],"strengthen":[185],"channel-wise":[188],"representations":[190],"improve":[192],"discriminative":[194],"performance.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}