{"id":"https://openalex.org/W4390660083","doi":"https://doi.org/10.1109/access.2024.3350880","title":"Deep Multilevel Cascade Residual Recurrent Framework (MCRR) for Sheet Music Recognition","display_name":"Deep Multilevel Cascade Residual Recurrent Framework (MCRR) for Sheet Music Recognition","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4390660083","doi":"https://doi.org/10.1109/access.2024.3350880"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3350880","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3350880","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10382526.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10382526.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074905992","display_name":"Ping Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I66883779","display_name":"Weifang University","ror":"https://ror.org/01frp7483","country_code":"CN","type":"education","lineage":["https://openalex.org/I66883779"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Yu","raw_affiliation_strings":["School of Music, Weifang University, Shandong, China","School of music, WeiFang University, shandong weifang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Music, Weifang University, Shandong, China","institution_ids":["https://openalex.org/I66883779"]},{"raw_affiliation_string":"School of music, WeiFang University, shandong weifang, China","institution_ids":["https://openalex.org/I66883779"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040422644","display_name":"Hailing Chen","orcid":"https://orcid.org/0009-0006-7790-4501"},"institutions":[{"id":"https://openalex.org/I4210136682","display_name":"Heze University","ror":"https://ror.org/041zje040","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136682"]},{"id":"https://openalex.org/I4210158777","display_name":"Academy of Music, Dance and Fine Arts","ror":"https://ror.org/05m7h6793","country_code":"BG","type":"education","lineage":["https://openalex.org/I4210158777"]}],"countries":["BG","CN"],"is_corresponding":false,"raw_author_name":"Hailing Chen","raw_affiliation_strings":["Music and Dance Academy, Heze University, Shandong, China","Music and Dance Academy, Heze University, shandong heze, China"],"raw_orcid":"https://orcid.org/0009-0006-7790-4501","affiliations":[{"raw_affiliation_string":"Music and Dance Academy, Heze University, Shandong, China","institution_ids":["https://openalex.org/I4210158777"]},{"raw_affiliation_string":"Music and Dance Academy, Heze University, shandong heze, China","institution_ids":["https://openalex.org/I4210136682"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.1262,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74388218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"12","issue":null,"first_page":"6941","last_page":"6960"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9986000061035156,"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":0.9986000061035156,"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/T13996","display_name":"Diverse Musicological Studies","score":0.9775000214576721,"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"}},{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9620000123977661,"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/computer-science","display_name":"Computer science","score":0.8520829677581787},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6765180826187134},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5205865502357483},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5155953764915466},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49748852849006653},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.46183347702026367},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4585738778114319},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4581195116043091},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4494946002960205},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4409511387348175},{"id":"https://openalex.org/keywords/music-information-retrieval","display_name":"Music information retrieval","score":0.4240916967391968},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35751956701278687},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.17943069338798523}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8520829677581787},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6765180826187134},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5205865502357483},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5155953764915466},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49748852849006653},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.46183347702026367},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4585738778114319},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4581195116043091},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4494946002960205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4409511387348175},{"id":"https://openalex.org/C2777946086","wikidata":"https://www.wikidata.org/wiki/Q1163335","display_name":"Music information retrieval","level":3,"score":0.4240916967391968},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35751956701278687},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.17943069338798523},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C558565934","wikidata":"https://www.wikidata.org/wiki/Q2743","display_name":"Musical","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3350880","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3350880","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10382526.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:be6c1662ce4a4743b6d0bd2c039e1bc8","is_oa":true,"landing_page_url":"https://doaj.org/article/be6c1662ce4a4743b6d0bd2c039e1bc8","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":"IEEE Access, Vol 12, Pp 6941-6960 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3350880","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3350880","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10382526.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390660083.pdf","grobid_xml":"https://content.openalex.org/works/W4390660083.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W108866686","https://openalex.org/W166419126","https://openalex.org/W1486322354","https://openalex.org/W1533221615","https://openalex.org/W1580215688","https://openalex.org/W1596846148","https://openalex.org/W1988387475","https://openalex.org/W2030863126","https://openalex.org/W2038510921","https://openalex.org/W2048023951","https://openalex.org/W2059304107","https://openalex.org/W2124698093","https://openalex.org/W2144499799","https://openalex.org/W2167059434","https://openalex.org/W2172987025","https://openalex.org/W2181689065","https://openalex.org/W2194775991","https://openalex.org/W2268051178","https://openalex.org/W2495415662","https://openalex.org/W2539290760","https://openalex.org/W2556788452","https://openalex.org/W2585360326","https://openalex.org/W2785514535","https://openalex.org/W2787229240","https://openalex.org/W2787721211","https://openalex.org/W2796517058","https://openalex.org/W2811221988","https://openalex.org/W2890011122","https://openalex.org/W2921712920","https://openalex.org/W2964310544","https://openalex.org/W3093190629","https://openalex.org/W3197999066","https://openalex.org/W4308860670","https://openalex.org/W4317502099","https://openalex.org/W4317565535","https://openalex.org/W4318351475","https://openalex.org/W4382602926","https://openalex.org/W4385990962","https://openalex.org/W4386977801","https://openalex.org/W6606040113","https://openalex.org/W6685651555","https://openalex.org/W6691828424","https://openalex.org/W6733550226","https://openalex.org/W6746786392","https://openalex.org/W6756616149","https://openalex.org/W6756786867","https://openalex.org/W6799085573","https://openalex.org/W6849105126","https://openalex.org/W6857192867","https://openalex.org/W7036311988"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W2790400419"],"abstract_inverted_index":{"Sheet":[0],"music":[1,28,40,52,64,91,123],"recognition":[2,65,73,92,176],"is":[3,59],"a":[4,48,62,69,143,183,291,358],"vital":[5],"technology":[6,24],"aimed":[7],"at":[8,332,372,383],"converting":[9],"printed":[10],"or":[11,17],"handwritten":[12],"musical":[13],"scores":[14],"into":[15,199],"digital":[16],"machine-readable":[18],"formats.":[19],"The":[20,262,323,375],"significance":[21],"of":[22,94,153,185,251,288,296,338,355,360],"this":[23],"lies":[25],"in":[26,120,136,268,276,303,312,320],"making":[27],"compositions":[29],"more":[30],"accessible":[31],"for":[32,51,89,222],"editing,":[33],"performance,":[34],"learning,":[35],"and":[36,43,55,75,108,114,132,156,174,227,232,237,245,272,290,306,314,317,357,364,369,398],"sharing,":[37],"thereby":[38,125],"fostering":[39],"education,":[41],"composition,":[42],"culture.":[44],"It":[45,351],"also":[46],"provides":[47],"powerful":[49],"tool":[50],"analysis,":[53,242],"research,":[54],"preservation.":[56],"Our":[57],"aim":[58],"to":[60,116,149,163,218,412],"investigate":[61],"sheet":[63,90,122],"method":[66,264,281,343,390],"that":[67],"offers":[68],"simple":[70],"workflow,":[71],"high":[72,301],"accuracy,":[74],"fast":[76],"model":[77,130,154,204],"convergence.":[78,205],"Specifically,":[79,275],"the":[80,95,121,127,137,151,158,169,209,220,249,252,270,277,280,336,339,342,404,413],"proposed":[81,253,263,414],"Deep":[82],"Multilevel":[83],"Cascade":[84],"Residual":[85],"Recurrent":[86,193],"(MCRR)":[87],"framework":[88],"consists":[93],"following":[96],"components.":[97],"Firstly,":[98],"we":[99,141,181,207,387],"introduce":[100],"additive":[101,105],"Gaussian":[102],"white":[103],"noise,":[104,107],"Perlin":[106],"elastic":[109],"deformations":[110],"such":[111],"as":[112],"rotation":[113],"stretching":[115],"simulate":[117],"real-world":[118],"noise":[119],"images,":[124],"augmenting":[126],"dataset,":[128,279,341],"enhancing":[129],"robustness,":[131],"mitigating":[133],"overfitting.":[134],"Secondly,":[135],"feature":[138,166,171],"extraction":[139,172],"phase,":[140],"employ":[142],"residual":[144],"Convolutional":[145],"Neural":[146,188],"Network":[147],"(ConvNet)":[148],"address":[150],"issue":[152],"degradation":[155],"use":[157,182],"multilevel":[159],"cascade":[160],"fusion":[161],"technique":[162],"obtain":[164],"comprehensive":[165],"information,":[167],"improving":[168],"model\u2019s":[170],"capability":[173],"reducing":[175],"errors.":[177],"For":[178],"note":[179,230,307,321,370],"recognition,":[180,308],"variant":[184],"RNN":[186],"(Recurrent":[187],"Network)":[189],"called":[190],"SRU":[191,217],"(Simple":[192],"Unit),":[194],"which":[195],"transforms":[196],"most":[197],"computations":[198],"parallel":[200],"processing,":[201],"speeding":[202],"up":[203],"Finally,":[206],"combine":[208],"Connectionist":[210],"Temporal":[211],"Classification":[212],"(CTC)":[213],"loss":[214],"function":[215],"with":[216,362,391],"eliminate":[219],"requirement":[221],"strict":[223],"alignment":[224],"between":[225],"data":[226],"labels,":[228],"enabling":[229],"classification":[231],"recognition.":[233],"Extensive":[234],"ablation":[235],"experiments":[236],"comparative":[238],"analyses,":[239],"including":[240],"visual":[241],"intuitive":[243],"illustrations,":[244],"quantitative":[246],"assessments,":[247],"confirm":[248],"effectiveness":[250],"method,":[254],"demonstrating":[255],"its":[256],"superiority":[257],"over":[258],"various":[259],"state-of-the-art":[260],"methods.":[261],"achieved":[265,344],"promising":[266],"results":[267],"both":[269],"PrIMus":[271,278],"Camera-PrIMuS":[273,340],"datasets.":[274],"obtained":[282],"an":[283,353],"SeER":[284,354],"(Symbol":[285],"Error":[286,294],"Rate)":[287,295],"1.4571%":[289],"SyER":[292,359],"(System":[293],"0.3234%.":[297],"Notably,":[298],"it":[299],"demonstrated":[300],"accuracy":[302,316,371],"pitch,":[304],"type,":[305],"scoring":[309],"approximately":[310,373],"97%":[311],"pitch":[313,363],"type":[315,365],"around":[318,367],"94%":[319],"accuracy.":[322],"training":[324,376],"time":[325,377],"per":[326,378],"epoch":[327,379],"was":[328,380],"relatively":[329],"low,":[330],"recorded":[331],"0.56":[333],"seconds.":[334],"In":[335],"case":[337],"slightly":[345,381],"lower":[346,409],"but":[347,407],"still":[348],"competitive":[349],"results.":[350],"exhibited":[352],"5.1488%":[356],"1.0612%,":[361],"accuracies":[366],"90%,":[368],"88%.":[374],"higher":[382],"1.93":[384],"seconds":[385],"Furthermore,":[386],"compare":[388],"our":[389],"existing":[392],"commercial":[393],"software,":[394],"namely":[395],"Capella-scan,":[396],"PhotoScore,":[397],"SmartScore.":[399],"Among":[400],"these,":[401],"Capella-scan":[402],"delivers":[403],"best":[405],"performance":[406],"exhibits":[408],"robustness":[410],"compared":[411],"method.":[415]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
