{"id":"https://openalex.org/W2083540857","doi":"https://doi.org/10.1145/1150402.1150523","title":"Understandable models Of music collections based on exhaustive feature generation with temporal statistics","display_name":"Understandable models Of music collections based on exhaustive feature generation with temporal statistics","publication_year":2006,"publication_date":"2006-08-20","ids":{"openalex":"https://openalex.org/W2083540857","doi":"https://doi.org/10.1145/1150402.1150523","mag":"2083540857"},"language":"en","primary_location":{"id":"doi:10.1145/1150402.1150523","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1150402.1150523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5022856816","display_name":"Fabian Moerchen","orcid":null},"institutions":[{"id":"https://openalex.org/I161103922","display_name":"Philipps University of Marburg","ror":"https://ror.org/01rdrb571","country_code":"DE","type":"education","lineage":["https://openalex.org/I161103922"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Moerchen","raw_affiliation_strings":["Philipps-University Marburg, Marburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Philipps-University Marburg, Marburg, Germany","institution_ids":["https://openalex.org/I161103922"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087498721","display_name":"Ingo Mierswa","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ingo Mierswa","raw_affiliation_strings":["University of Dortmund, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Dortmund, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078097779","display_name":"Alfred Ultsch","orcid":"https://orcid.org/0000-0002-7845-3283"},"institutions":[{"id":"https://openalex.org/I161103922","display_name":"Philipps University of Marburg","ror":"https://ror.org/01rdrb571","country_code":"DE","type":"education","lineage":["https://openalex.org/I161103922"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alfred Ultsch","raw_affiliation_strings":["Philipps-University Marburg, Marburg, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Philipps-University Marburg, Marburg, Germany","institution_ids":["https://openalex.org/I161103922"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8803,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.93706752,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"882","last_page":"891"},"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":0.9950000047683716,"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":0.9878000020980835,"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/computer-science","display_name":"Computer science","score":0.7672691345214844},{"id":"https://openalex.org/keywords/timbre","display_name":"Timbre","score":0.6938143968582153},{"id":"https://openalex.org/keywords/polyphony","display_name":"Polyphony","score":0.638445258140564},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.612566351890564},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5372410416603088},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.49321240186691284},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4880343973636627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44239017367362976},{"id":"https://openalex.org/keywords/music-information-retrieval","display_name":"Music information retrieval","score":0.4257428050041199},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41995853185653687},{"id":"https://openalex.org/keywords/musical","display_name":"Musical","score":0.389962762594223},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34880369901657104},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3452893793582916},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11817330121994019}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7672691345214844},{"id":"https://openalex.org/C2776539107","wikidata":"https://www.wikidata.org/wiki/Q176501","display_name":"Timbre","level":3,"score":0.6938143968582153},{"id":"https://openalex.org/C128979739","wikidata":"https://www.wikidata.org/wiki/Q179465","display_name":"Polyphony","level":2,"score":0.638445258140564},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.612566351890564},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5372410416603088},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.49321240186691284},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4880343973636627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44239017367362976},{"id":"https://openalex.org/C2777946086","wikidata":"https://www.wikidata.org/wiki/Q1163335","display_name":"Music information retrieval","level":3,"score":0.4257428050041199},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41995853185653687},{"id":"https://openalex.org/C558565934","wikidata":"https://www.wikidata.org/wiki/Q2743","display_name":"Musical","level":2,"score":0.389962762594223},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34880369901657104},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3452893793582916},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11817330121994019},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","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},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1150402.1150523","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1150402.1150523","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.66.5948","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.5948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-ai.cs.uni-dortmund.de/DOKUMENTE/moerchen_etal_2006a.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.92.8709","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.92.8709","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mybytes.de/papers/moerchen06understandable.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W2873872","https://openalex.org/W9714568","https://openalex.org/W28412257","https://openalex.org/W92099993","https://openalex.org/W131293899","https://openalex.org/W134184673","https://openalex.org/W153995565","https://openalex.org/W1480376833","https://openalex.org/W1512098439","https://openalex.org/W1517729176","https://openalex.org/W1534477342","https://openalex.org/W1545406001","https://openalex.org/W1554944419","https://openalex.org/W1560013842","https://openalex.org/W1797428404","https://openalex.org/W1835965142","https://openalex.org/W1989077545","https://openalex.org/W2002330943","https://openalex.org/W2032360374","https://openalex.org/W2048488652","https://openalex.org/W2053156826","https://openalex.org/W2057613716","https://openalex.org/W2062170755","https://openalex.org/W2096735762","https://openalex.org/W2097807493","https://openalex.org/W2099760476","https://openalex.org/W2102650424","https://openalex.org/W2106393550","https://openalex.org/W2119288237","https://openalex.org/W2123497171","https://openalex.org/W2125055259","https://openalex.org/W2126410803","https://openalex.org/W2133824856","https://openalex.org/W2135046866","https://openalex.org/W2140539262","https://openalex.org/W2143235280","https://openalex.org/W2159557803","https://openalex.org/W2165533158","https://openalex.org/W2166183437","https://openalex.org/W2168441989","https://openalex.org/W2188843906","https://openalex.org/W2545979229","https://openalex.org/W3036383388","https://openalex.org/W3143639557","https://openalex.org/W4285719527","https://openalex.org/W4291439952","https://openalex.org/W4299429830","https://openalex.org/W6605236737","https://openalex.org/W6605549694","https://openalex.org/W6631021127","https://openalex.org/W6632590417","https://openalex.org/W6633431331","https://openalex.org/W6687166713"],"related_works":["https://openalex.org/W2411659965","https://openalex.org/W2387677326","https://openalex.org/W4200063482","https://openalex.org/W2357575019","https://openalex.org/W1481643945","https://openalex.org/W2153275212","https://openalex.org/W2402674073","https://openalex.org/W126167045","https://openalex.org/W2328848613","https://openalex.org/W4298140471"],"abstract_inverted_index":{"Data":[0],"mining":[1],"in":[2,96],"large":[3,111],"collections":[4],"of":[5,19,23,30,37,52,59,84,143,150,154,166],"polyphonic":[6,53],"music":[7,54],"has":[8,63],"recently":[9],"received":[10],"increasing":[11],"interest":[12],"by":[13],"companies":[14],"along":[15],"with":[16,115],"the":[17,28,35,44,57],"advent":[18],"commercial":[20],"online":[21],"distribution":[22],"music.":[24,85,155],"Important":[25],"applications":[26],"include":[27],"categorization":[29],"songs":[31,38],"into":[32,146],"genres":[33],"and":[34,43,62,135,183],"recommendation":[36],"according":[39],"to":[40,103,119,139,179],"musical":[41,46,124],"similarity":[42],"customer's":[45],"preferences.":[47],"Modeling":[48],"genre":[49,92],"or":[50,95],"timbre":[51],"is":[55,101],"at":[56],"core":[58],"these":[60,144,174],"tasks":[61],"been":[64,74],"recognized":[65],"as":[66,185,187],"a":[67,91,141,147,151,162],"difficult":[68],"problem.":[69],"Many":[70],"audio":[71],"features":[72,122,145,176],"have":[73],"proposed,":[75],"but":[76],"they":[77],"do":[78,87],"not":[79,88],"provide":[80],"easily":[81],"understandable":[82,182],"descriptions":[83],"They":[86],"explain":[89],"why":[90],"was":[93],"chosen":[94],"which":[97],"way":[98],"one":[99],"song":[100],"similar":[102],"another.":[104],"We":[105,126],"present":[106],"an":[107],"approach":[108],"that":[109],"combines":[110],"scale":[112],"feature":[113,129],"generation":[114,130],"meta":[116],"learning":[117],"techniques":[118],"obtain":[120],"meaningful":[121],"for":[123],"similarity.":[125],"perform":[127],"exhaustive":[128],"based":[131,172],"on":[132,173],"temporal":[133],"statistics":[134],"train":[136],"regression":[137],"models":[138,159,171],"summarize":[140],"subset":[142],"single":[148],"descriptor":[149],"particular":[152],"notion":[153],"Using":[156],"several":[157],"such":[158],"we":[160],"produce":[161],"concise":[163],"semantic":[164,175],"description":[165],"each":[167],"song.":[168],"Genre":[169],"classification":[170],"are":[177],"shown":[178],"be":[180],"better":[181],"almost":[184],"accurate":[186],"traditional":[188],"methods.":[189]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
