{"id":"https://openalex.org/W7140112254","doi":"https://doi.org/10.3390/bdcc10030096","title":"Hybrid Music Similarity with Hypergraph and Siamese Network","display_name":"Hybrid Music Similarity with Hypergraph and Siamese Network","publication_year":2026,"publication_date":"2026-03-21","ids":{"openalex":"https://openalex.org/W7140112254","doi":"https://doi.org/10.3390/bdcc10030096"},"language":"en","primary_location":{"id":"doi:10.3390/bdcc10030096","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc10030096","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/bdcc10030096","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130410971","display_name":"Sera Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sera Kim","raw_affiliation_strings":["Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130362350","display_name":"Youngjun Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I157264075","display_name":"Sangmyung University","ror":"https://ror.org/01x4whx42","country_code":"KR","type":"education","lineage":["https://openalex.org/I157264075"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngjun Kim","raw_affiliation_strings":["Department of Computer Science, Sangmyung University, Seoul 03016, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-0968-4157","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Sangmyung University, Seoul 03016, Republic of Korea","institution_ids":["https://openalex.org/I157264075"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jaewon Lee","orcid":"https://orcid.org/0000-0003-4557-1387"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaewon Lee","raw_affiliation_strings":["Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4557-1387","affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045766402","display_name":"Dalwon Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dalwon Jang","raw_affiliation_strings":["Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Seongnam 13449, Republic of Korea","institution_ids":["https://openalex.org/I4210131650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37959021,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"3","first_page":"96","last_page":"96"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9908999800682068,"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":0.9908999800682068,"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/T10788","display_name":"Neuroscience and Music Perception","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.0008999999845400453,"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/similarity","display_name":"Similarity (geometry)","score":0.7548999786376953},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5655999779701233},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5579000115394592},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.49480000138282776},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4758000075817108},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4733999967575073},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43479999899864197},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.414000004529953}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7548999786376953},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6298999786376953},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5655999779701233},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5579000115394592},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4758000075817108},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46549999713897705},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43479999899864197},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C2777946086","wikidata":"https://www.wikidata.org/wiki/Q1163335","display_name":"Music information retrieval","level":3,"score":0.40380001068115234},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3183000087738037},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C87687168","wikidata":"https://www.wikidata.org/wiki/Q173114","display_name":"Digital audio","level":4,"score":0.25540000200271606},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/bdcc10030096","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc10030096","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d6e73286659e4916834abe68b979ce3d","is_oa":true,"landing_page_url":"https://doaj.org/article/d6e73286659e4916834abe68b979ce3d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data and Cognitive Computing, Vol 10, Iss 3, p 96 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/bdcc10030096","is_oa":true,"landing_page_url":"https://doi.org/10.3390/bdcc10030096","pdf_url":null,"source":{"id":"https://openalex.org/S4210238752","display_name":"Big Data and Cognitive Computing","issn_l":"2504-2289","issn":["2504-2289"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data and Cognitive Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G384784951","display_name":null,"funder_award_id":"RS-2023-00224136","funder_id":"https://openalex.org/F4320323890","funder_display_name":"Korea Creative Content Agency"}],"funders":[{"id":"https://openalex.org/F4320323890","display_name":"Korea Creative Content Agency","ror":"https://ror.org/036vyg793"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1690919088","https://openalex.org/W1915070853","https://openalex.org/W1978317329","https://openalex.org/W2009415795","https://openalex.org/W2054141820","https://openalex.org/W2133824856","https://openalex.org/W2155106456","https://openalex.org/W2158515176","https://openalex.org/W2171960770","https://openalex.org/W2733963828","https://openalex.org/W2807021761","https://openalex.org/W2914721378","https://openalex.org/W2945827670","https://openalex.org/W3045200674","https://openalex.org/W3192055963","https://openalex.org/W4232430330","https://openalex.org/W4241598686","https://openalex.org/W4315977496","https://openalex.org/W4321462979","https://openalex.org/W4366460030","https://openalex.org/W4372266552","https://openalex.org/W4376274690","https://openalex.org/W4386837604","https://openalex.org/W4391026746","https://openalex.org/W4391174974","https://openalex.org/W4393090165","https://openalex.org/W4401843903","https://openalex.org/W4402527021","https://openalex.org/W4403221479","https://openalex.org/W4403221743","https://openalex.org/W4404522911","https://openalex.org/W4408021498","https://openalex.org/W4409076702","https://openalex.org/W4414034830"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1,23],"proposes":[2,24],"a":[3,25,81,87,120,181],"novel":[4],"method":[5,26],"for":[6,18,27,37,180,262],"measuring":[7,28],"music":[8,11,19,32,38,41,133,256,264,267],"similarity.":[9],"Existing":[10],"similarity":[12,30,49,53,84,89,157,178,200,254],"measurements":[13],"have":[14],"often":[15,44],"been":[16],"used":[17,36],"appreciation,":[20],"but":[21],"this":[22,75],"the":[29,77,99,140,172,188,203,220,230,244],"between":[31,132],"samples":[33,134,257],"which":[34,56],"are":[35,71,115,126,165,227],"production.":[39],"Conventional":[40],"recommendation":[42,62,173,265],"approaches":[43],"rely":[45],"on":[46,148,209,229],"either":[47],"metadata-based":[48],"or":[50],"audio-based":[51],"feature":[52],"in":[54,60,119,202,266],"isolation,":[55],"limits":[57],"their":[58,136],"effectiveness":[59],"sample-based":[61,263],"scenarios":[63],"where":[64,223],"both":[65,250],"compositional":[66],"context":[67],"and":[68,95,113,122,135,150,168,197,225,252,258],"acoustic":[69],"characteristics":[70],"important.":[72],"To":[73],"address":[74],"limitation,":[76],"proposed":[78,189,245],"framework":[79,247],"combines":[80],"hypergraph-based":[82],"information":[83],"module":[85,90,142],"with":[86],"feature-based":[88,141],"learned":[91,127],"using":[92],"Siamese":[93,145],"networks":[94,146],"triplet":[96],"loss.":[97],"In":[98,138],"information-based":[100],"module,":[101],"metadata":[102],"attributes":[103,196],"such":[104],"as":[105,117],"beats":[106],"per":[107],"minute":[108],"(BPM),":[109],"genre,":[110],"chord,":[111],"key,":[112],"instrument":[114,149],"modeled":[116],"vertices":[118],"hypergraph,":[121],"Random":[123],"Walk\u2013Word2Vec":[124],"embeddings":[125],"to":[128,154,175,237],"capture":[129],"structural":[130,251],"relationships":[131],"attributes.":[137],"parallel,":[139],"employs":[143],"vertex-specific":[144],"trained":[147,166],"key":[151],"classification":[152],"tasks":[153],"learn":[155],"perceptual":[156,253],"directly":[158],"from":[159],"audio":[160],"signals.":[161],"The":[162],"two":[163],"modules":[164],"independently":[167],"jointly":[169],"utilized":[170],"at":[171],"stage":[174],"provide":[176],"attribute-specific":[177],"results":[179,214,241],"given":[182],"query":[183],"sample.":[184],"Results":[185],"show":[186],"that":[187,243],"system":[190],"achieves":[191],"high":[192],"Precision@k":[193],"across":[194],"multiple":[195],"forms":[198],"stable":[199],"structures":[201],"embedding":[204,216],"space,":[205],"even":[206],"without":[207],"relying":[208],"user":[210],"interaction":[211],"data.":[212],"These":[213,240],"reflect":[215],"consistency":[217],"evaluated":[218],"over":[219],"entire":[221],"dataset":[222],"training":[224],"retrieval":[226],"performed":[228],"same":[231],"sample":[232],"pool,":[233],"rather":[234],"than":[235],"generalization":[236],"unseen":[238],"samples.":[239],"demonstrate":[242],"hybrid":[246],"effectively":[248],"captures":[249],"among":[255],"is":[259],"well":[260],"suited":[261],"production":[268],"environments.":[269]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-24T00:00:00"}