{"id":"https://openalex.org/W2734580238","doi":"https://doi.org/10.1109/taffc.2017.2724515","title":"A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification","display_name":"A Novel Method Based on OMPGW Method for Feature Extraction in Automatic Music Mood Classification","publication_year":2017,"publication_date":"2017-07-10","ids":{"openalex":"https://openalex.org/W2734580238","doi":"https://doi.org/10.1109/taffc.2017.2724515","mag":"2734580238"},"language":"en","primary_location":{"id":"doi:10.1109/taffc.2017.2724515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2017.2724515","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Affective Computing","raw_type":"journal-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/A5078214911","display_name":"Shasha Mo","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shasha Mo","raw_affiliation_strings":["State Key Laboratory for Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory for Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053316728","display_name":"Jianwei Niu","orcid":"https://orcid.org/0000-0003-3946-5107"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Niu","raw_affiliation_strings":["State Key Laboratory for Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3946-5107","affiliations":[{"raw_affiliation_string":"State Key Laboratory for Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.4614,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.83386311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"10","issue":"3","first_page":"313","last_page":"324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9998999834060669,"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":0.9998999834060669,"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/T10860","display_name":"Speech and Audio Processing","score":0.9984999895095825,"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":0.9761999845504761,"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":0.638980507850647},{"id":"https://openalex.org/keywords/music-information-retrieval","display_name":"Music information retrieval","score":0.6163077354431152},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5849568843841553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5697532892227173},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5510856509208679},{"id":"https://openalex.org/keywords/mood","display_name":"Mood","score":0.5234729051589966},{"id":"https://openalex.org/keywords/matching-pursuit","display_name":"Matching pursuit","score":0.49923086166381836},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4820462167263031},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4804653525352478},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.47592073678970337},{"id":"https://openalex.org/keywords/gabor-transform","display_name":"Gabor transform","score":0.45287853479385376},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.362191379070282},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.14750781655311584},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10436376929283142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.638980507850647},{"id":"https://openalex.org/C2777946086","wikidata":"https://www.wikidata.org/wiki/Q1163335","display_name":"Music information retrieval","level":3,"score":0.6163077354431152},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5849568843841553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5697532892227173},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5510856509208679},{"id":"https://openalex.org/C2780733359","wikidata":"https://www.wikidata.org/wiki/Q331769","display_name":"Mood","level":2,"score":0.5234729051589966},{"id":"https://openalex.org/C156872377","wikidata":"https://www.wikidata.org/wiki/Q6786281","display_name":"Matching pursuit","level":3,"score":0.49923086166381836},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4820462167263031},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4804653525352478},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.47592073678970337},{"id":"https://openalex.org/C173149727","wikidata":"https://www.wikidata.org/wiki/Q996397","display_name":"Gabor transform","level":4,"score":0.45287853479385376},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.362191379070282},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.14750781655311584},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10436376929283142},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"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/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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taffc.2017.2724515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taffc.2017.2724515","pdf_url":null,"source":{"id":"https://openalex.org/S104780363","display_name":"IEEE Transactions on Affective Computing","issn_l":"1949-3045","issn":["1949-3045","2371-9850"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Affective Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1809830838","display_name":"\u878d\u5408\u4e0a\u4e0b\u6587\u548c\u7528\u6237\u5c5e\u6027\u7684\u793e\u4ea4\u591a\u5a92\u4f53\u60c5\u611f\u5206\u6790\u53ca\u4f20\u64ad\u6a21\u5f0f\u7814\u7a76","funder_award_id":"61572060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G840777561","display_name":"\u9762\u5411\u7f51\u7edc\u89c6\u9891\u76f4\u64ad\u7684\u654f\u611f\u4fe1\u606f\u5b9e\u65f6\u68c0\u6d4b\u6280\u672f\u7814\u7a76","funder_award_id":"61772060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W1495408943","https://openalex.org/W1509691205","https://openalex.org/W1555511196","https://openalex.org/W1575659433","https://openalex.org/W1866281937","https://openalex.org/W1943383135","https://openalex.org/W1963947028","https://openalex.org/W1983507146","https://openalex.org/W1988060680","https://openalex.org/W2047380763","https://openalex.org/W2057498692","https://openalex.org/W2064772252","https://openalex.org/W2065829287","https://openalex.org/W2070154071","https://openalex.org/W2076608692","https://openalex.org/W2077466407","https://openalex.org/W2079735306","https://openalex.org/W2087407704","https://openalex.org/W2089705003","https://openalex.org/W2098902891","https://openalex.org/W2099760476","https://openalex.org/W2103134137","https://openalex.org/W2107089078","https://openalex.org/W2107430826","https://openalex.org/W2108945853","https://openalex.org/W2109080095","https://openalex.org/W2111730426","https://openalex.org/W2114416396","https://openalex.org/W2115055618","https://openalex.org/W2119821739","https://openalex.org/W2125324924","https://openalex.org/W2128196382","https://openalex.org/W2128659236","https://openalex.org/W2130426352","https://openalex.org/W2133824856","https://openalex.org/W2136623223","https://openalex.org/W2138445519","https://openalex.org/W2140539262","https://openalex.org/W2140856955","https://openalex.org/W2148600927","https://openalex.org/W2151693816","https://openalex.org/W2167969117","https://openalex.org/W2169704631","https://openalex.org/W2171848217","https://openalex.org/W2343847964","https://openalex.org/W2592535880","https://openalex.org/W2602528134","https://openalex.org/W2916566921","https://openalex.org/W2938558181","https://openalex.org/W3165898261","https://openalex.org/W4239510810","https://openalex.org/W4241319329","https://openalex.org/W6629710146","https://openalex.org/W6633310046","https://openalex.org/W6634411194","https://openalex.org/W6639031803","https://openalex.org/W6640188369","https://openalex.org/W6664918268","https://openalex.org/W6675150577","https://openalex.org/W6677292325","https://openalex.org/W6679470320","https://openalex.org/W6684826812","https://openalex.org/W6712935731","https://openalex.org/W6735017652","https://openalex.org/W6941198223"],"related_works":["https://openalex.org/W1981874526","https://openalex.org/W2094896467","https://openalex.org/W2390244057","https://openalex.org/W2606578966","https://openalex.org/W2382555176","https://openalex.org/W2360182727","https://openalex.org/W2384007269","https://openalex.org/W1905998934","https://openalex.org/W2362576574","https://openalex.org/W4317791936"],"abstract_inverted_index":{"Music":[0],"mood":[1,161,203,214],"is":[2,28,40,84,131,170],"useful":[3],"for":[4,30,99],"music-related":[5],"applications":[6],"such":[7],"as":[8],"music":[9,20,31,93,120,160,202,206],"retrieval":[10],"or":[11],"recommendation,":[12],"which":[13,39],"represents":[14],"the":[15,35,43,50,57,64,66,69,73,76,102,119,124,127,135,140,143,152,157,174,178,182,198,229],"inherent":[16],"emotional":[17],"expression":[18],"of":[19,37,61,92,118,139,154,159,213,219],"signals.":[21,94],"In":[22,123,201],"this":[23],"paper,":[24],"a":[25,107,115],"novel":[26],"technique":[27,180],"proposed":[29,85,103,179,199],"signal":[32,100,121],"analysis":[33],"in":[34],"view":[36],"emotions,":[38],"based":[41,165],"on":[42,166,222],"orthogonal":[44,77],"matching":[45,78],"pursuit,":[46],"Gabor":[47,82],"functions,":[48],"and":[49,68,110,113,192,216],"Wigner":[51,128],"distribution":[52,129,138],"function.":[53],"The":[54],"technique,":[55],"called":[56],"OMPGW":[58,230],"method,":[59],"consists":[60],"three-level":[62],"schemes:":[63],"low-level,":[65],"middle-level":[67,125],"high-level":[70],"schemes.":[71,145],"For":[72],"low-level":[74,144],"schemes,":[75,126],"pursuit":[79],"combined":[80],"with":[81,96,177,189,197],"functions":[83],"to":[86,133,150,172],"provide":[87],"an":[88],"adaptive":[89],"time-frequency":[90,136],"decomposition":[91],"Compared":[95],"other":[97],"algorithms":[98],"analysis,":[101],"algorithm":[104],"can":[105,225],"achieve":[106],"higher":[108],"spatial":[109],"temporal":[111],"resolution":[112],"give":[114],"better":[116,193],"interpret":[117],"structures.":[122],"function":[130],"applied":[132],"obtain":[134],"energy":[137],"results":[141,194],"from":[142],"High-level":[146],"schemes":[147],"are":[148,187,195,208],"used":[149],"describe":[151],"modeling":[153],"audio":[155],"features,":[156],"procedure":[158],"classification.":[162],"A":[163],"classifier":[164],"support":[167],"vector":[168],"machines":[169],"utilized":[171],"model":[173],"extracted":[175],"features":[176],"regarding":[181],"emotion":[183],"models.":[184],"Several":[185],"experiments":[186],"conducted":[188],"four":[190],"datasets,":[191],"achieved":[196,227],"method.":[200,231],"classification":[204],"experiments,":[205],"clips":[207],"classified":[209],"into":[210],"different":[211],"kinds":[212],"clusters,":[215],"mean":[217],"accuracy":[218],"69.53":[220],"percent":[221],"our":[223],"dataset":[224],"be":[226],"using":[228]},"counts_by_year":[{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}