{"id":"https://openalex.org/W7166727307","doi":"https://doi.org/10.48550/arxiv.2606.29901","title":"Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss","display_name":"Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166727307","doi":"https://doi.org/10.48550/arxiv.2606.29901"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.29901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.29901","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101841879","display_name":"Nian Shao","orcid":"https://orcid.org/0000-0002-6260-3005"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Nian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139671436","display_name":"Xian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139697459","display_name":"Xiaofei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaofei","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/T11309","display_name":"Music and Audio Processing","score":0.961899995803833,"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":0.961899995803833,"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":0.009100000374019146,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.006300000008195639,"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/leverage","display_name":"Leverage (statistics)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5403000116348267},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.47450000047683716},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.37389999628067017},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.258899986743927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7348999977111816},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5065000057220459},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44449999928474426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39320001006126404},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2775999903678894},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.29901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.29901","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.29901","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sound":[0],"event":[1],"detection":[2],"(SED)":[3],"is":[4,15,89],"a":[5,53,110,153],"core":[6],"module":[7],"for":[8],"acoustic":[9],"environmental":[10],"analysis,":[11],"yet":[12],"its":[13],"performance":[14],"often":[16],"limited":[17,39],"by":[18,67,75],"scarce":[19],"labeled":[20,36],"data.":[21],"Recent":[22],"systems":[23],"leverage":[24],"large":[25],"pretrained":[26],"audio":[27],"foundation":[28],"models,":[29],"but":[30],"effective":[31],"fine-tuning":[32,57],"remains":[33],"challenging":[34],"because":[35],"data":[37,42,84],"are":[38,43],"while":[40,104],"unlabeled":[41,83],"abundant.":[44],"A":[45],"previous":[46],"work,":[47,61],"ATST-SED,":[48],"addressed":[49],"this":[50,60,114],"problem":[51],"with":[52],"pseudo-label":[54,99],"based":[55],"semi-supervised":[56,129],"framework.":[58],"In":[59],"we":[62,116],"further":[63],"improve":[64],"the":[65,96,133,148,157],"framework":[66,130],"adopting":[68],"an":[69],"embedding-level":[70,135],"self-supervised":[71],"contrastive":[72,79,105,136],"loss":[73],"inspired":[74],"ATST-Frame":[76],"pretraining.":[77],"This":[78],"objective":[80],"better":[81],"exploits":[82],"during":[85],"fine-tuning.":[86],"One":[87],"challenge":[88],"that":[90],"mixup":[91,108,123,126],"serves":[92],"different":[93],"roles":[94],"in":[95,127],"two":[97],"objectives:":[98],"learning":[100,106],"uses":[101],"composition":[102,122],"mixup,":[103,119],"treats":[107],"as":[109],"perturbation.":[111],"To":[112],"resolve":[113],"mismatch,":[115],"propose":[117],"conditional":[118],"which":[120],"combines":[121],"and":[124,131,144],"perturbation":[125],"one":[128],"defines":[132],"corresponding":[134],"losses.":[137],"The":[138],"resulting":[139],"model":[140],"achieves":[141],"0.645":[142],"PSDS1":[143],"0.822":[145],"PSDS2":[146],"on":[147],"DESED":[149],"validation":[150],"set,":[151],"establishing":[152],"new":[154],"state":[155],"of":[156],"art.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}