{"id":"https://openalex.org/W4388117841","doi":"https://doi.org/10.23919/eusipco58844.2023.10289936","title":"HSP-TL: A Deep Metric Learning Model with Triplet Loss for Hit Song Prediction","display_name":"HSP-TL: A Deep Metric Learning Model with Triplet Loss for Hit Song Prediction","publication_year":2023,"publication_date":"2023-09-04","ids":{"openalex":"https://openalex.org/W4388117841","doi":"https://doi.org/10.23919/eusipco58844.2023.10289936"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco58844.2023.10289936","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco58844.2023.10289936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"type":"article","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/A5006082842","display_name":"Petros Vavaroutsos","orcid":"https://orcid.org/0000-0003-1929-5649"},"institutions":[{"id":"https://openalex.org/I4210126545","display_name":"PostScriptum (Greece)","ror":"https://ror.org/0305q4m96","country_code":"GR","type":"company","lineage":["https://openalex.org/I4210126545"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Petros Vavaroutsos","raw_affiliation_strings":["Orfium Research,Athens,Greece","Orfium Research, Athens, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orfium Research,Athens,Greece","institution_ids":["https://openalex.org/I4210126545"]},{"raw_affiliation_string":"Orfium Research, Athens, Greece","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048033327","display_name":"Pantelis Vikatos","orcid":"https://orcid.org/0000-0003-1573-5125"},"institutions":[{"id":"https://openalex.org/I4210126545","display_name":"PostScriptum (Greece)","ror":"https://ror.org/0305q4m96","country_code":"GR","type":"company","lineage":["https://openalex.org/I4210126545"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Pantelis Vikatos","raw_affiliation_strings":["Orfium Research,Athens,Greece","Orfium Research, Athens, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orfium Research,Athens,Greece","institution_ids":["https://openalex.org/I4210126545"]},{"raw_affiliation_string":"Orfium Research, Athens, Greece","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5543,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.65048904,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"146","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":1.0,"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":1.0,"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":0.9965999722480774,"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"}},{"id":"https://openalex.org/T11113","display_name":"Music History and Culture","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/1210","display_name":"Music"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lyrics","display_name":"Lyrics","score":0.9467778205871582},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.7448593974113464},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7332330942153931},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6176837682723999},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5191242098808289},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5029057860374451},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49988484382629395},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.49857401847839355},{"id":"https://openalex.org/keywords/dash","display_name":"Dash","score":0.46242445707321167},{"id":"https://openalex.org/keywords/popular-music","display_name":"Popular music","score":0.4611518681049347},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4602375030517578},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4386104941368103},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38529154658317566},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3608149290084839},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.1424298882484436},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.09738662838935852},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09157508611679077}],"concepts":[{"id":"https://openalex.org/C2776436406","wikidata":"https://www.wikidata.org/wiki/Q602446","display_name":"Lyrics","level":2,"score":0.9467778205871582},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.7448593974113464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7332330942153931},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6176837682723999},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5191242098808289},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5029057860374451},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49988484382629395},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.49857401847839355},{"id":"https://openalex.org/C2776090536","wikidata":"https://www.wikidata.org/wiki/Q187819","display_name":"Dash","level":2,"score":0.46242445707321167},{"id":"https://openalex.org/C114611597","wikidata":"https://www.wikidata.org/wiki/Q373342","display_name":"Popular music","level":2,"score":0.4611518681049347},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4602375030517578},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4386104941368103},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38529154658317566},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3608149290084839},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1424298882484436},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.09738662838935852},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09157508611679077},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"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/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/eusipco58844.2023.10289936","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco58844.2023.10289936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 31st European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2385545","https://openalex.org/W47828995","https://openalex.org/W80219283","https://openalex.org/W1781845406","https://openalex.org/W2013126184","https://openalex.org/W2571568675","https://openalex.org/W2591643707","https://openalex.org/W2765176396","https://openalex.org/W2803235822","https://openalex.org/W2896457183","https://openalex.org/W2902796839","https://openalex.org/W2963775347","https://openalex.org/W2990635052","https://openalex.org/W3008421074","https://openalex.org/W3029475517","https://openalex.org/W3086040963","https://openalex.org/W3099206234","https://openalex.org/W3208837796","https://openalex.org/W3209840666","https://openalex.org/W4286901894","https://openalex.org/W6600110733","https://openalex.org/W6601976940","https://openalex.org/W6603339013","https://openalex.org/W6638109833","https://openalex.org/W6731439077","https://openalex.org/W6734289096","https://openalex.org/W6744836587","https://openalex.org/W6755207826","https://openalex.org/W6756560125","https://openalex.org/W6770710749","https://openalex.org/W6785269020","https://openalex.org/W6803378345","https://openalex.org/W6811445250"],"related_works":["https://openalex.org/W2360952181","https://openalex.org/W2067430219","https://openalex.org/W4379348330","https://openalex.org/W2964472689","https://openalex.org/W2263813420","https://openalex.org/W2377530523","https://openalex.org/W2062196734","https://openalex.org/W2491013962","https://openalex.org/W2762717821","https://openalex.org/W2080906825"],"abstract_inverted_index":{"The":[0,156],"music":[1,34],"industry":[2],"is":[3,124],"interested":[4],"in":[5,15],"the":[6,20,74,81,84,90,110,127,136,145,160],"future":[7],"success":[8,75],"of":[9,76,83,138,147],"a":[10,24,51,77],"song":[11,150],"and":[12,36,65,70,152],"its":[13],"presence":[14],"popular":[16],"rankings":[17],"such":[18,32],"as":[19,33],"Billboard":[21],"charts.":[22],"However,":[23],"song's":[25],"popularity":[26],"might":[27],"be":[28],"impacted":[29],"by":[30,163],"variables":[31],"trends":[35],"social":[37],"influences,":[38],"which":[39,132],"are":[40],"indifferent":[41],"to":[42,55,72,88,109,165],"audio":[43,69,106],"signals.":[44],"In":[45],"this":[46],"paper,":[47],"we":[48,98,133],"present":[49],"HSP-TL,":[50],"deep":[52],"learning":[53],"model,":[54],"identify":[56],"likely":[57],"hit":[58],"songs.":[59],"Our":[60,122,141],"work":[61],"combines":[62],"temporal":[63],"information":[64],"features":[66],"derived":[67],"from":[68],"lyrics":[71,137,148],"estimate":[73],"recording.":[78],"We":[79,113],"adopt":[80],"concept":[82],"triplet":[85],"loss":[86],"function":[87],"minimize":[89],"distance":[91],"between":[92],"objects":[93],"with":[94,135],"similar":[95],"popularity.":[96],"Also,":[97],"use":[99,114],"convolutional":[100],"neural":[101],"networks":[102],"on":[103,126],"2-D":[104],"low-level":[105],"features,":[107],"contrary":[108],"current":[111,161],"approach.":[112],"pre-trained":[115],"language":[116],"models":[117],"for":[118],"text-based":[119],"feature":[120],"extraction.":[121],"method":[123],"evaluated":[125],"Hit":[128],"Song":[129],"Prediction":[130],"Dataset,":[131],"enrich":[134],"each":[139],"song.":[140],"results":[142],"show":[143],"that":[144],"inclusion":[146],"improves":[149],"uniqueness":[151],"reflects":[153],"musical":[154],"trends.":[155],"proposed":[157],"model":[158],"outperforms":[159],"approach":[162],"up":[164],"8%.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}