{"id":"https://openalex.org/W4312416532","doi":"https://doi.org/10.1109/iwaenc53105.2022.9914753","title":"Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder","display_name":"Accelerated Unsupervised Clustering in Acoustic Sensor Networks Using Federated Learning and a Variational Autoencoder","publication_year":2022,"publication_date":"2022-09-05","ids":{"openalex":"https://openalex.org/W4312416532","doi":"https://doi.org/10.1109/iwaenc53105.2022.9914753"},"language":"en","primary_location":{"id":"doi:10.1109/iwaenc53105.2022.9914753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc53105.2022.9914753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Workshop on Acoustic Signal Enhancement (IWAENC)","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/A5004668688","display_name":"Luca Becker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luca Becker","raw_affiliation_strings":["Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024357851","display_name":"Alexandru Nelus","orcid":"https://orcid.org/0000-0002-4926-6796"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexandru Nelus","raw_affiliation_strings":["Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042603152","display_name":"Rene Glitza","orcid":"https://orcid.org/0009-0002-6437-5912"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rene Glitza","raw_affiliation_strings":["Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064880163","display_name":"Rainer Martin","orcid":"https://orcid.org/0000-0002-9587-4215"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rainer Martin","raw_affiliation_strings":["Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ruhr-Universit&#x00E4;t Bochum,Institute of Communication Acoustics,Bochum,Germany","institution_ids":[]}]}],"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":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994999766349792,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.9547997713088989},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8581407070159912},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7354284524917603},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.6812048554420471},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5824527740478516},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4793884754180908},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.47350484132766724},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4687274694442749},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42172369360923767},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3817659318447113},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37075746059417725},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34227412939071655},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.31440895795822144},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13657456636428833}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9547997713088989},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8581407070159912},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354284524917603},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.6812048554420471},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5824527740478516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4793884754180908},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.47350484132766724},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4687274694442749},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42172369360923767},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3817659318447113},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37075746059417725},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34227412939071655},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31440895795822144},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13657456636428833},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwaenc53105.2022.9914753","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iwaenc53105.2022.9914753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Workshop on Acoustic Signal Enhancement (IWAENC)","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":21,"referenced_works":["https://openalex.org/W1494198834","https://openalex.org/W1969209815","https://openalex.org/W2044893557","https://openalex.org/W2071608040","https://openalex.org/W2114779530","https://openalex.org/W2605510096","https://openalex.org/W2787726203","https://openalex.org/W2889581931","https://openalex.org/W2915938453","https://openalex.org/W2978015420","https://openalex.org/W3016169665","https://openalex.org/W3038205844","https://openalex.org/W3080934299","https://openalex.org/W3162294994","https://openalex.org/W3166393642","https://openalex.org/W3177173743","https://openalex.org/W3201529712","https://openalex.org/W4205365644","https://openalex.org/W6640963894","https://openalex.org/W6736609796","https://openalex.org/W6766360866"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W4220926404","https://openalex.org/W2806873178","https://openalex.org/W3123344745","https://openalex.org/W2770818364","https://openalex.org/W2965146396"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3,34,75],"present":[4],"an":[5],"accelerated":[6],"algorithm":[7,43],"for":[8,48],"clustering":[9,93],"source-dominated":[10],"microphones":[11,25],"in":[12,92],"acoustic":[13],"sensor":[14],"networks.":[15],"Predicated":[16],"on":[17],"privacy-preserving":[18],"unsupervised":[19],"clustered":[20],"federated":[21],"learning":[22],"that":[23],"groups":[24],"by":[26],"evaluating":[27],"the":[28,42,53,70,86],"similarity":[29],"of":[30,56,60,81,88],"model":[31],"weight":[32],"updates,":[33],"introduce":[35],"a":[36,77,89],"light-weight":[37],"variational":[38],"autoencoder":[39],"and":[40,58,65],"equip":[41],"with":[44],"supplementary":[45],"control":[46],"criteria":[47],"faster":[49],"convergence.":[50],"We":[51],"validate":[52],"quality,":[54],"degree":[55],"acceleration":[57],"utility":[59],"our":[61],"method":[62],"using":[63],"clustering-based":[64],"classification-based":[66],"tasks.":[67],"Compared":[68],"to":[69],"previously":[71],"employed":[72],"deterministic":[73],"autoencoder,":[74],"observe":[76],"significantly":[78],"lower":[79],"number":[80],"client-server":[82],"communication":[83],"rounds":[84],"at":[85],"price":[87],"minor":[90],"reduction":[91],"performance.":[94]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
