{"id":"https://openalex.org/W7138224010","doi":"https://doi.org/10.48550/arxiv.2603.13749","title":"Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection","display_name":"Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138224010","doi":"https://doi.org/10.48550/arxiv.2603.13749"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13749","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13749","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":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.2603.13749","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129653661","display_name":"Phurich Saengthong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saengthong, Phurich","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103015161","display_name":"Takahiro Shinozaki","orcid":"https://orcid.org/0000-0001-8114-8450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shinozaki, Takahiro","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.550599992275238,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.550599992275238,"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/T11309","display_name":"Music and Audio Processing","score":0.22709999978542328,"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/T13018","display_name":"Seismology and Earthquake Studies","score":0.04500000178813934,"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/robustness","display_name":"Robustness (evolution)","score":0.6848000288009644},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6470999717712402},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5859000086784363},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.477400004863739},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4681999981403351},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.35679998993873596},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.35580000281333923},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3495999872684479},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.3479999899864197}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6848000288009644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6470999717712402},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5859000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5703999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49869999289512634},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4681999981403351},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.38420000672340393},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3172999918460846},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C2983980114","wikidata":"https://www.wikidata.org/wiki/Q4854529","display_name":"Noise spectrum","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13749","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13749","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":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.2603.13749","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13749","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7561034560203552}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"subtle":[1],"deviations":[2],"in":[3,100,132],"noisy":[4],"acoustic":[5],"environments":[6],"is":[7,72],"central":[8],"to":[9,61,79,112,126,149],"anomalous":[10],"sound":[11],"detection":[12],"(ASD).":[13],"A":[14],"common":[15],"training-free":[16],"ASD":[17],"pipeline":[18],"temporally":[19,96],"pools":[20],"frame-level":[21],"representations":[22],"into":[23],"a":[24,32,54,75,101,122],"band-preserving":[25],"feature":[26],"vector":[27],"and":[28,87,108,116,151,154],"scores":[29,111],"anomalies":[30],"using":[31],"single":[33,55],"nearest-neighbor":[34],"match.":[35],"However,":[36],"this":[37],"global":[38,56],"matching":[39,71],"can":[40],"inflate":[41],"normal-score":[42,114],"variance":[43],"through":[44],"two":[45],"effects.":[46],"First,":[47],"when":[48,157],"normal":[49,84],"sounds":[50],"exhibit":[51],"band-wise":[52],"variability,":[53],"neighbor":[57],"forces":[58],"all":[59],"bands":[60,78],"share":[62],"the":[63],"same":[64],"reference,":[65],"increasing":[66],"band-level":[67],"mismatch.":[68],"Second,":[69],"cosine-based":[70],"energy-coupled,":[73],"allowing":[74],"few":[76],"high-energy":[77],"dominate":[80],"score":[81],"computation":[82],"under":[83],"energy":[85],"fluctuations":[86],"further":[88,120],"increase":[89],"variance.":[90],"We":[91,119],"propose":[92],"BEAM,":[93],"which":[94],"stores":[95],"pooled":[97],"sub-band":[98,133],"vectors":[99],"memory":[102],"bank,":[103],"retrieves":[104],"neighbors":[105],"per":[106],"sub-band,":[107],"uniformly":[109],"aggregates":[110],"reduce":[113],"variability":[115],"improve":[117],"discriminability.":[118],"introduce":[121],"parameter-free":[123],"adaptive":[124],"fusion":[125],"better":[127],"handle":[128],"diverse":[129],"temporal":[130],"dynamics":[131],"responses.":[134],"Experiments":[135],"on":[136],"multiple":[137],"DCASE":[138],"Task":[139],"2":[140],"benchmarks":[141],"show":[142],"strong":[143],"performance":[144],"without":[145],"task-specific":[146],"training,":[147],"robustness":[148],"noise":[150],"domain":[152],"shifts,":[153],"complementary":[155],"gains":[156],"combined":[158],"with":[159],"encoder":[160],"fine-tuning.":[161]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}
