{"id":"https://openalex.org/W649768112","doi":null,"title":"Signal Processing Methods for Audio Classification and Music Content Analysis","display_name":"Signal Processing Methods for Audio Classification and Music Content Analysis","publication_year":2009,"publication_date":"2009-06-25","ids":{"openalex":"https://openalex.org/W649768112","mag":"649768112"},"language":"en","primary_location":{"id":"pmh:oai:trepo.tuni.fi:10024/114712","doi":null,"is_oa":true,"landing_page_url":"https://trepo.tuni.fi//handle/10024/114712","pdf_url":"https://trepo.tuni.fi//handle/10024/114712","source":{"id":"https://openalex.org/S4306401860","display_name":"Tampere University Institutional Repository (Tampere University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I150589677","host_organization_name":"Tampere University of Applied Sciences","host_organization_lineage":["https://openalex.org/I150589677"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"fi=Artikkeliv\u00e4it\u00f6skirja | en=Doctoral dissertation (article-based) |"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://trepo.tuni.fi//handle/10024/114712","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076376566","display_name":"Antti Eronen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Antti Eronen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5076376566"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":10,"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":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":0.0,"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/T10860","display_name":"Speech and Audio Processing","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/audio-classification","display_name":"audio classification","score":0.9169999957084656},{"id":"https://openalex.org/keywords/music-content-analysis","display_name":"music content analysis","score":0.8529999852180481},{"id":"https://openalex.org/keywords/musical-instrument-recognition","display_name":"instrument identification","score":0.9890000224113464},{"id":"https://openalex.org/keywords/context-recognition","display_name":"context recognition","score":0.8460000157356262},{"id":"https://openalex.org/keywords/tempo-estimation","display_name":"tempo estimation","score":0.7900000214576721},{"id":"https://openalex.org/keywords/accent-detection","display_name":"accent detection","score":0.4090000092983246},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"independent component analysis","score":0.8270000219345093},{"id":"https://openalex.org/keywords/discriminative-training","display_name":"discriminative training","score":0.871999979019165},{"id":"https://openalex.org/keywords/hidden-markov-models","display_name":"hidden Markov models","score":0.7979999780654907},{"id":"https://openalex.org/keywords/mel-frequency-cepstral-coefficients","display_name":"Mel-frequency cepstral coefficients","score":0.7870000004768372},{"id":"https://openalex.org/keywords/phase-estimation","display_name":"phase estimation","score":0.5839999914169312},{"id":"https://openalex.org/keywords/music-information-retrieval","display_name":"music information retrieval","score":0.42500001192092896}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5757799744606018},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.5615621209144592},{"id":"https://openalex.org/C127220857","wikidata":"https://www.wikidata.org/wiki/Q2719318","display_name":"Audio signal processing","level":4,"score":0.5415760278701782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5375826358795166},{"id":"https://openalex.org/C87687168","wikidata":"https://www.wikidata.org/wiki/Q173114","display_name":"Digital audio","level":4,"score":0.4839901924133301},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.44755321741104126},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.42715984582901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35960328578948975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33213526010513306},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.324195921421051},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.2951257824897766},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.2557854652404785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22859546542167664},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:trepo.tuni.fi:10024/114712","doi":null,"is_oa":true,"landing_page_url":"https://trepo.tuni.fi//handle/10024/114712","pdf_url":"https://trepo.tuni.fi//handle/10024/114712","source":{"id":"https://openalex.org/S4306401860","display_name":"Tampere University Institutional Repository (Tampere University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I150589677","host_organization_name":"Tampere University of Applied Sciences","host_organization_lineage":["https://openalex.org/I150589677"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"fi=Artikkeliv\u00e4it\u00f6skirja | en=Doctoral dissertation (article-based) |"},{"id":"mag:649768112","doi":null,"is_oa":false,"landing_page_url":"https://trepo.tuni.fi/handle/10024/114712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:trepo.tuni.fi:10024/114712","doi":null,"is_oa":true,"landing_page_url":"https://trepo.tuni.fi//handle/10024/114712","pdf_url":"https://trepo.tuni.fi//handle/10024/114712","source":{"id":"https://openalex.org/S4306401860","display_name":"Tampere University Institutional Repository (Tampere University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I150589677","host_organization_name":"Tampere University of Applied Sciences","host_organization_lineage":["https://openalex.org/I150589677"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"fi=Artikkeliv\u00e4it\u00f6skirja | en=Doctoral dissertation (article-based) |"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321855","display_name":"Tekes","ror":"https://ror.org/02ag8cq23"},{"id":"https://openalex.org/F4320322426","display_name":"Nokia Foundation","ror":"https://ror.org/0401nzk46"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W649768112.pdf","grobid_xml":"https://content.openalex.org/works/W649768112.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2097574082","https://openalex.org/W7096322190","https://openalex.org/W1797428404","https://openalex.org/W2395157499","https://openalex.org/W3120114888","https://openalex.org/W2133401807","https://openalex.org/W3005277267","https://openalex.org/W1620668332","https://openalex.org/W2129788248","https://openalex.org/W7160605395"],"abstract_inverted_index":{"Signal":[0],"processing":[1],"methods":[2,94,106,175,186,264],"for":[3,97,214,372,418,562],"audio":[4,37,54,86,169,194],"classification":[5,16,38,174,195,475],"and":[6,44,101,116,142,155,173,178,202,208,298,312,330,338,348,374,377,380,569,593,630],"music":[7,91,98,136,167,419,428],"content":[8,92,159],"analysis":[9,100,105,271,365,421],"are":[10,39,95,150,176,221,265,318],"developed":[11],"in":[12,119,135,427,474,567,609],"this":[13,228],"thesis.":[14],"Audio":[15],"is":[17,71,129,138,249,370,395,402,433,462,481,527,579,598,621],"here":[18],"understood":[19],"as":[20,206,529],"the":[21,48,50,58,66,88,108,111,132,140,152,189,216,225,233,254,258,279,286,293,300,335,346,349,357,382,385,400,405,413,425,437,454,458,465,476,499,504,512,518,521,534,542,589,601,610,614],"process":[22],"of":[23,36,90,110,114,122,126,146,191,227,257,295,315,334,384,399,412,439,460,467,506,520,533,603,606],"assigning":[24],"a":[25,77,120,147,192,236,296,313,321,410,468,482,488,508,530,558,564,575,624],"discrete":[26,483],"category":[27,484],"label":[28],"to":[29,57,130,187,223,235,251,292,355,404,452,463,478,492,502,632],"an":[30,53,74,308,493,617],"unknown":[31,494],"recording.":[32],"Two":[33,219],"specific":[34],"problems":[35],"considered:":[40],"musical":[41,181,260,301,328],"instrument":[42,182],"recognition":[43,255],"context":[45],"recognition.":[46],"In":[47,180],"former,":[49],"system":[51,196],"classifies":[52],"recording":[55],"according":[56],"instrument,":[59],"e.g.":[60],"violin,":[61],"flute,":[62],"piano,":[63],"that":[64,161,197,457,541,578],"produced":[65],"sound.":[67],"The":[68,124,267,304,351,397,431,524,572],"latter":[69],"task":[70],"about":[72],"classifying":[73],"environment,":[75],"such":[76],"car,":[78],"restaurant,":[79],"or":[80],"library,":[81],"based":[82,390,434],"on":[83,344,391,423,435],"its":[84],"ambient":[85],"background.\\n\\nIn":[87],"field":[89],"analysis,":[93,262],"presented":[96],"meter":[99,261,270],"chorus":[102,127,133,565,619],"detection.":[103],"Meter":[104],"consider":[107,185],"estimation":[109,333,549],"regular":[112],"pattern":[113],"strong":[115],"weak":[117],"beats":[118],"piece":[121],"music.":[123,571],"goal":[125,353,459],"detection":[128,602],"locate":[131],"segment":[134],"which":[137,290,325],"often":[139],"catchiest":[141],"most":[143,153],"memorable":[144],"part":[145],"song.":[148],"These":[149],"among":[151],"important":[154],"readily":[156],"commercially":[157],"applicable":[158],"attributes":[160],"can":[162],"be":[163,479],"automatically":[164],"analyzed":[165],"from":[166,307,511],"signals.\\n\\nFor":[168],"classification,":[170,453],"several":[171,628],"features":[172,234,305],"proposed":[177,222,371,543],"evaluated.":[179],"recognition,":[183],"we":[184,556],"improve":[188,253],"performance":[190,226,519],"baseline":[193,229],"uses":[198],"mel-frequency":[199,590],"cepstral":[200,591],"coefficients":[201],"their":[203],"first":[204,268,406],"derivatives":[205],"features,":[207],"continuous-density":[209],"hidden":[210],"Markov":[211],"models":[212],"(HMMs)":[213],"modeling":[215,369],"feature":[217,310],"distributions.":[218],"improvements":[220],"increase":[224,503],"system.":[230],"First,":[231],"transforming":[232],"base":[237],"with":[238,409],"maximal":[239],"statistical":[240],"independence":[241],"using":[242,442,588,623],"independent":[243],"component":[244],"analysis.":[245],"Secondly,":[246],"discriminative":[247],"training":[248,513],"shown":[250],"further":[252,633],"accuracy":[256,398,426],"system.\\n\\nFor":[259],"three":[263,274,552],"proposed.":[266],"performs":[269,331],"jointly":[272],"at":[273,278,285,299],"different":[275],"time":[276],"scales:":[277],"temporally":[280],"atomic":[281],"tatum":[282,375],"pulse":[283,288],"level,":[284,289],"tactus":[287],"corresponds":[291],"tempo":[294,429,438,525,548],"piece,":[297],"measure":[302,339],"level.":[303],"obtained":[306,580],"accent":[309],"analyzer":[311],"bank":[314],"combfilter":[316],"resonators":[317],"processed":[319],"by":[320,581,600],"novel":[322,388],"probabilistic":[323,368],"model":[324],"represents":[326],"primitive":[327],"knowledge":[329],"joint":[332],"tatum,":[336],"tactus,":[337],"pulses.\\n\\nThe":[340],"second":[341],"method":[342,358,401,407,417,432,522,544,561,573],"focuses":[343,422],"estimating":[345,436],"beat":[347,373],"tatum.":[350],"design":[352],"was":[354],"keep":[356],"computationally":[359,559],"very":[360],"efficient":[361,560],"while":[362],"retaining":[363],"sufficient":[364],"accuracy.":[366],"Simplified":[367],"period":[376],"phase":[378],"estimation,":[379],"ensuring":[381],"continuity":[383],"estimates.":[386],"A":[387],"phase-estimator":[389],"adaptive":[392],"comb":[393],"filtering":[394],"presented.":[396],"close":[403],"but":[408],"fraction":[411],"computational":[414],"cost.\\n\\nThe":[415],"third":[416],"rhythm":[420],"improving":[424],"estimation.":[430],"periodicity":[440,495],"vectors":[441],"locally":[443],"weighted":[444],"k-Nearest":[445],"Neighbors":[446],"(k-NN)":[447],"regression.":[448],"Regression":[449],"closely":[450],"relates":[451],"difference":[455],"being":[456],"regression":[461],"estimate":[464,526],"value":[466,477],"continuous":[469],"variable":[470],"(the":[471],"tempo),":[472],"whereas":[473],"assigned":[480],"label.":[485],"We":[486],"propose":[487],"resampling":[489],"step":[490,516],"applied":[491],"vector":[496],"before":[497],"finding":[498,507],"nearest":[500,535],"neighbors":[501],"likelihood":[505],"good":[509],"match":[510],"set.":[514],"This":[515,597],"improves":[517],"significantly.":[523],"computed":[528],"distance-weighted":[531],"median":[532],"neighbor":[536],"tempi.":[537],"Experimental":[538],"results":[539],"show":[540],"provides":[545],"significantly":[546],"better":[547],"accuracies":[550],"than":[551],"reference":[553],"methods.":[554],"\\n\\nFinally,":[555],"describe":[557],"detecting":[563],"section":[566,620],"popular":[568],"rock":[570],"utilizes":[574],"self-dissimilarity":[576],"representation":[577],"summing":[582],"two":[583],"separate":[584],"distance":[585,608,611],"matrices":[586],"calculated":[587],"coefficient":[592],"pitch":[594],"chroma":[595],"features.":[596],"followed":[599],"off-diagonal":[604],"segments":[605],"small":[607],"matrix.":[612],"From":[613],"detected":[615],"segments,":[616],"initial":[618],"selected":[622],"scoring":[625],"mechanism":[626],"utilizing":[627],"heuristics,":[629],"subjected":[631],"processing.":[634]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-08-31T07:19:18.363097","created_date":"2016-06-24T00:00:00"}
