{"id":"https://openalex.org/W6911855524","doi":"https://doi.org/10.5281/zenodo.14877351","title":"Green MIR? Investigating Computational Cost of Recent Music-Ai Research in ISMIR","display_name":"Green MIR? Investigating Computational Cost of Recent Music-Ai Research in ISMIR","publication_year":2024,"publication_date":"2024-11-10","ids":{"openalex":"https://openalex.org/W6911855524","doi":"https://doi.org/10.5281/zenodo.14877351"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.14877351","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14877351","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":""},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.14877351","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Andre Holzapfel","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Andre Holzapfel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Anna-Kaisa Kaila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anna-Kaisa Kaila","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Petra J\u00e4\u00e4skel\u00e4inen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petra J\u00e4\u00e4skel\u00e4inen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35668473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.16990000009536743,"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"}},"topics":[{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.16990000009536743,"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/T11309","display_name":"Music and Audio Processing","score":0.12700000405311584,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07150000333786011,"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/generative-grammar","display_name":"Generative grammar","score":0.6061000227928162},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5443000197410583},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5098000168800354},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5074999928474426},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.5034000277519226},{"id":"https://openalex.org/keywords/carbon-footprint","display_name":"Carbon footprint","score":0.46700000762939453},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.44359999895095825},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4311000108718872},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.4221000075340271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6333000063896179},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6061000227928162},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5315999984741211},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5074999928474426},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4675999879837036},{"id":"https://openalex.org/C2780936489","wikidata":"https://www.wikidata.org/wiki/Q310667","display_name":"Carbon footprint","level":3,"score":0.46700000762939453},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.44359999895095825},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.4221000075340271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40290001034736633},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.336899995803833},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C3019966295","wikidata":"https://www.wikidata.org/wiki/Q1341368","display_name":"Energy cost","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2833000123500824},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.2825999855995178},{"id":"https://openalex.org/C83516724","wikidata":"https://www.wikidata.org/wiki/Q234173","display_name":"Ecological footprint","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C164749845","wikidata":"https://www.wikidata.org/wiki/Q320389","display_name":"Environmental impact assessment","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.14877351","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14877351","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":""}],"best_oa_location":{"id":"doi:10.5281/zenodo.14877351","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.14877351","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":""},"sustainable_development_goals":[{"score":0.7751054763793945,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"environmental":[1,78],"footprint":[2,158],"of":[3,18,42,47,59,127,138,159,176,194],"Generative":[4],"AI":[5],"and":[6,21,69,103,116,180,188],"other":[7,166],"Deep":[8],"Learning":[9],"(DL)":[10],"technologies":[11],"is":[12,40],"increasing.":[13],"To":[14],"understand":[15],"the":[16,19,43,48,61,95,108,135,156,160,173,182,186],"scale":[17],"problem":[20],"to":[22,77,142,165],"identify":[23],"solutions":[24],"for":[25,150],"avoiding":[26],"excessive":[27],"energy":[28,45,139,157],"use":[29],"in":[30,94,129,163,185,191],"DL":[31],"research":[32,63],"at":[33,145],"communities":[34],"such":[35],"as":[36,147],"ISMIR,":[37,146],"more":[38,152],"knowledge":[39],"needed":[41],"current":[44],"cost":[46],"undertaken":[49],"research.":[50],"In":[51],"this":[52,192],"paper,":[53],"we":[54,171],"provide":[55],"a":[56,82,125,148],"scoping":[57],"inquiry":[58],"how":[60],"ISMIR":[62,91,105,161],"concerning":[64],"automatic":[65],"music":[66,71,113],"generation":[67],"(AMG)":[68],"computing-heavy":[70],"analysis":[72,114],"currently":[73],"discloses":[74],"information":[75],"related":[76,141],"impact.":[79],"We":[80],"present":[81],"study":[83,123],"based":[84],"on":[85],"two":[86],"corpora":[87],"that":[88,98,111],"document":[89],"1)":[90],"papers":[92,106],"published":[93],"years":[96,109],"2017\u20132023":[97],"introduce":[99],"an":[100],"AMG":[101],"model,":[102],"2)":[104],"from":[107],"2022\u20132023":[110],"propose":[112],"models":[115],"include":[117],"heavy":[118],"computations":[119],"with":[120],"GPUs.":[121],"Our":[122],"demonstrates":[124],"lack":[126],"transparency":[128],"model":[130,143,178,189],"training":[131,144],"documentation.":[132],"It":[133],"provides":[134],"first":[136],"estimates":[137,154],"consumption":[140],"baseline":[149],"making":[151],"systematic":[153],"about":[155],"conference":[162],"relation":[164],"machine":[167],"learning":[168],"events.":[169],"Furthermore,":[170],"map":[172],"geographical":[174],"distribution":[175],"generative":[177],"contributions":[179],"discuss":[181],"corporate":[183],"role":[184],"funding":[187],"choices":[190],"body":[193],"work.":[195]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}