{"id":"https://openalex.org/W7164413588","doi":"https://doi.org/10.48550/arxiv.2606.11903","title":"Snapping Matters: Context-Aware Onset Refinement for Automatic Music Transcription","display_name":"Snapping Matters: Context-Aware Onset Refinement for Automatic Music Transcription","publication_year":2026,"publication_date":"2026-06-10","ids":{"openalex":"https://openalex.org/W7164413588","doi":"https://doi.org/10.48550/arxiv.2606.11903"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.11903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138479960","display_name":"Abhirup Saha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saha, Abhirup","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134124325","display_name":"Hans-Ulrich Berendes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berendes, Hans-Ulrich","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138449219","display_name":"Meinard M\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00fcller, Meinard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066394268","display_name":"Ben Maman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maman, Ben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9760000109672546,"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":0.9760000109672546,"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":0.008299999870359898,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.006000000052154064,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.5803999900817871},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.5583999752998352},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.5310999751091003},{"id":"https://openalex.org/keywords/transcription","display_name":"Transcription (linguistics)","score":0.46880000829696655},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4352000057697296},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4239000082015991},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.37950000166893005},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3555999994277954}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6951000094413757},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.5803999900817871},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.5583999752998352},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.5310999751091003},{"id":"https://openalex.org/C179926584","wikidata":"https://www.wikidata.org/wiki/Q207714","display_name":"Transcription (linguistics)","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34540000557899475},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.3301999866962433},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29840001463890076},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C68859911","wikidata":"https://www.wikidata.org/wiki/Q1503724","display_name":"Pattern matching","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C72802188","wikidata":"https://www.wikidata.org/wiki/Q1683352","display_name":"Smith\u2013Waterman algorithm","level":5,"score":0.2757999897003174},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2734000086784363},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.11903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11903","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Precise":[0],"note-level":[1],"annotations":[2,28],"are":[3,32,188],"critical":[4],"for":[5,29,111,120,178],"training":[6,80,112],"automatic":[7],"music":[8],"transcription":[9,170],"(AMT)":[10],"systems,":[11],"in":[12,65],"particular":[13],"note-onset":[14],"labels,":[15],"which":[16],"form":[17],"a":[18,48,66,93,105,133],"core":[19],"component":[20],"of":[21,108],"many":[22],"recent":[23],"AMT":[24],"systems.":[25],"However,":[26],"high-quality":[27],"real-world":[30],"recordings":[31,164],"scarce.":[33],"Sequence-level":[34],"score--audio":[35,76],"alignment":[36,168],"methods":[37],"such":[38],"as":[39,57,92,132],"dynamic":[40],"time":[41],"warping":[42],"provide":[43],"only":[44],"coarse":[45],"correspondence,":[46],"making":[47],"local":[49,101],"refinement":[50,54,150],"step":[51],"necessary.":[52],"This":[53],"step,":[55],"known":[56],"snapping,":[58,174],"adjusts":[59],"aligned":[60,75,124],"score":[61],"onsets":[62],"using":[63],"peaks":[64],"neural":[67],"onset":[68,146,167],"posteriorgram":[69],"and":[70,97,137,152,162,169,182],"often":[71],"determines":[72],"whether":[73],"weakly":[74,123],"pairs":[77],"become":[78],"usable":[79],"data":[81],"at":[82],"all.":[83],"Despite":[84],"its":[85],"practical":[86],"importance,":[87],"snapping":[88,109,117,131,180],"is":[89,118],"typically":[90],"treated":[91],"simple":[94],"post-processing":[95],"heuristic":[96],"implemented":[98],"with":[99,175],"greedy":[100,173],"decisions.":[102],"We":[103],"present":[104],"systematic":[106],"analysis":[107],"strategies":[110],"instrument-agnostic":[113],"transcribers,":[114],"demonstrating":[115],"that":[116],"essential":[119],"learning":[121],"from":[122],"data.":[125],"Building":[126],"on":[127,190],"this,":[128],"we":[129],"formulate":[130],"per-pitch":[134],"assignment":[135],"problem":[136],"solve":[138],"it":[139],"via":[140],"bipartite":[141],"graph":[142],"matching,":[143],"yielding":[144],"context-aware":[145],"decisions":[147],"under":[148],"overlapping":[149],"windows":[151,181],"uncertain":[153],"initial":[154,184],"alignments.":[155,185],"Extensive":[156],"cross-dataset":[157],"experiments":[158],"across":[159],"piano,":[160],"chamber,":[161],"orchestral":[163],"show":[165],"improved":[166],"accuracy":[171],"over":[172],"gains":[176],"increasing":[177],"wider":[179],"coarser":[183],"Qualitative":[186],"examples":[187],"provided":[189],"our":[191],"project":[192],"page:":[193],"https://abhirupsaha8.github.io":[194]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-12T00:00:00"}