{"id":"https://openalex.org/W6967721952","doi":"https://doi.org/10.5281/zenodo.10265289","title":"On the Performance of Optical Music Recognition in the Absence of Specific Training Data","display_name":"On the Performance of Optical Music Recognition in the Absence of Specific Training Data","publication_year":2023,"publication_date":"2023-11-04","ids":{"openalex":"https://openalex.org/W6967721952","doi":"https://doi.org/10.5281/zenodo.10265289"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.10265289","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265289","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.10265289","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Juan C. Martinez-Sevilla","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Juan C. Martinez-Sevilla","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Adri\u00e1n Rosell\u00f3","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adri\u00e1n Rosell\u00f3","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"David Rizo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Rizo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Jorge Calvo-Zaragoza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jorge Calvo-Zaragoza","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4075,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.61834116,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"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/T11309","display_name":"Music and Audio Processing","score":0.9943000078201294,"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":0.9943000078201294,"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":0.0008999999845400453,"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"}},{"id":"https://openalex.org/T10788","display_name":"Neuroscience and Music Perception","score":0.0005000000237487257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6428999900817871},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.628000020980835},{"id":"https://openalex.org/keywords/notation","display_name":"Notation","score":0.5159000158309937},{"id":"https://openalex.org/keywords/musical","display_name":"Musical","score":0.5133000016212463},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4997999966144562},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4867999851703644},{"id":"https://openalex.org/keywords/conjunction","display_name":"Conjunction (astronomy)","score":0.4844000041484833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110999822616577},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6428999900817871},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.628000020980835},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C558565934","wikidata":"https://www.wikidata.org/wiki/Q2743","display_name":"Musical","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4997999966144562},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.4844000041484833},{"id":"https://openalex.org/C88639978","wikidata":"https://www.wikidata.org/wiki/Q233861","display_name":"Musical notation","level":3,"score":0.4763999879360199},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47099998593330383},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3862999975681305},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.33410000801086426},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33379998803138733},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2922999858856201},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.29179999232292175},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C2777946086","wikidata":"https://www.wikidata.org/wiki/Q1163335","display_name":"Music information retrieval","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2621000111103058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.10265289","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265289","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.10265289","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.10265289","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":[{"display_name":"Quality Education","score":0.5855902433395386,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Optical":[0],"Music":[1],"Recognition":[2],"(OMR)":[3],"has":[4],"become":[5],"a":[6,84,151],"popular":[7],"technology":[8],"to":[9,38,52,64,100,104,110,116,135,149],"retrieve":[10],"information":[11],"present":[12],"in":[13,16,30,46,95,106,119,161],"musical":[14,47],"scores":[15],"conjunction":[17],"with":[18,91,158],"the":[19,28,31,41,53],"increasing":[20],"improvement":[21],"of":[22,130,153],"Deep":[23],"Learning":[24],"techniques,":[25],"which":[26,71],"represent":[27],"state-of-the-art":[29],"field.":[32],"However,":[33],"its":[34],"effectiveness":[35],"is":[36,44,72],"limited":[37],"cases":[39],"where":[40],"target":[42],"collection":[43],"similar":[45],"context":[48],"and":[49,74,86,133],"graphical":[50],"appearance":[51],"available":[54],"training":[55,131],"examples.":[56],"To":[57],"address":[58],"this":[59,80],"limitation,":[60],"researchers":[61],"have":[62,105],"resorted":[63],"labeling":[65],"examples":[66],"for":[67,89],"specific":[68,123],"neural":[69],"models,":[70],"time-consuming":[73],"raises":[75],"questions":[76],"about":[77],"usability.":[78],"In":[79],"study,":[81],"we":[82],"propose":[83],"holistic":[85],"comprehensive":[87],"study":[88],"dealing":[90,157],"new":[92,159],"music":[93],"collections":[94,117,160],"OMR,":[96],"including":[97],"extensive":[98],"experiments":[99,140],"identify":[101],"key":[102],"aspects":[103],"mind":[107],"that":[108,145],"lead":[109],"better":[111],"performance":[112],"ratios.":[113],"We":[114],"resort":[115],"written":[118],"Mensural":[120],"notation":[121],"as":[122],"use":[124],"case,":[125],"comprising":[126],"5":[127],"different":[128],"corpora":[129],"domains":[132],"up":[134],"15":[136],"test":[137],"collections.":[138],"Our":[139],"report":[141],"many":[142],"interesting":[143],"insights":[144],"will":[146],"be":[147],"important":[148],"create":[150],"manual":[152],"best":[154],"practices":[155],"when":[156],"OMR":[162],"systems.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}