{"id":"https://openalex.org/W3165870784","doi":"https://doi.org/10.3934/mfc.2021008","title":"Evaluation of parallel and sequential deep learning models for music subgenre classification","display_name":"Evaluation of parallel and sequential deep learning models for music subgenre classification","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3165870784","doi":"https://doi.org/10.3934/mfc.2021008","mag":"3165870784"},"language":"en","primary_location":{"id":"doi:10.3934/mfc.2021008","is_oa":true,"landing_page_url":"https://doi.org/10.3934/mfc.2021008","pdf_url":"https://www.aimsciences.org/article/exportPdf?id=7c98eeb9-8e6e-45a5-bbc3-5675de0c76f5","source":{"id":"https://openalex.org/S4210238810","display_name":"Mathematical Foundations of Computing","issn_l":"2577-8838","issn":["2577-8838"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315844","host_organization_name":"American Institute of Mathematical Sciences","host_organization_lineage":["https://openalex.org/P4310315844"],"host_organization_lineage_names":["American Institute of Mathematical Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mathematical Foundations of Computing","raw_type":"journal-article"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.aimsciences.org/article/exportPdf?id=7c98eeb9-8e6e-45a5-bbc3-5675de0c76f5","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033105122","display_name":"Miria Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miria Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101530176","display_name":"Wenying Feng","orcid":"https://orcid.org/0000-0003-1329-4426"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenying Feng","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":1.1092,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.7535014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"4","issue":"2","first_page":"131","last_page":"131"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9980999827384949,"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":0.9970999956130981,"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/initialization","display_name":"Initialization","score":0.7706300020217896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6934210658073425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5594702959060669},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5579869151115417},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.5403701066970825},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5388502478599548},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5003619194030762},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.4930410087108612},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4752461910247803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4439693093299866},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3719151020050049},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.324098140001297}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7706300020217896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934210658073425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5594702959060669},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5579869151115417},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.5403701066970825},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5388502478599548},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5003619194030762},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.4930410087108612},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4752461910247803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4439693093299866},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3719151020050049},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.324098140001297},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3934/mfc.2021008","is_oa":true,"landing_page_url":"https://doi.org/10.3934/mfc.2021008","pdf_url":"https://www.aimsciences.org/article/exportPdf?id=7c98eeb9-8e6e-45a5-bbc3-5675de0c76f5","source":{"id":"https://openalex.org/S4210238810","display_name":"Mathematical Foundations of Computing","issn_l":"2577-8838","issn":["2577-8838"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315844","host_organization_name":"American Institute of Mathematical Sciences","host_organization_lineage":["https://openalex.org/P4310315844"],"host_organization_lineage_names":["American Institute of Mathematical Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mathematical Foundations of Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3934/mfc.2021008","is_oa":true,"landing_page_url":"https://doi.org/10.3934/mfc.2021008","pdf_url":"https://www.aimsciences.org/article/exportPdf?id=7c98eeb9-8e6e-45a5-bbc3-5675de0c76f5","source":{"id":"https://openalex.org/S4210238810","display_name":"Mathematical Foundations of Computing","issn_l":"2577-8838","issn":["2577-8838"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315844","host_organization_name":"American Institute of Mathematical Sciences","host_organization_lineage":["https://openalex.org/P4310315844"],"host_organization_lineage_names":["American Institute of Mathematical Sciences"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Mathematical Foundations of Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7300000190734863,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W15066456","https://openalex.org/W1508756421","https://openalex.org/W1526622935","https://openalex.org/W1533861849","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2097807493","https://openalex.org/W2107789863","https://openalex.org/W2133824856","https://openalex.org/W2147800946","https://openalex.org/W2155944992","https://openalex.org/W2163605009","https://openalex.org/W2293924895","https://openalex.org/W2563031223","https://openalex.org/W2910489255","https://openalex.org/W2953311167","https://openalex.org/W2963451564","https://openalex.org/W2964121744","https://openalex.org/W2990096955","https://openalex.org/W3000748319","https://openalex.org/W6634903227"],"related_works":["https://openalex.org/W2530685530","https://openalex.org/W4375868962","https://openalex.org/W2011227383","https://openalex.org/W2088854863","https://openalex.org/W1976719989","https://openalex.org/W2942893872","https://openalex.org/W2065606036","https://openalex.org/W3179495260","https://openalex.org/W3127543252","https://openalex.org/W2016904525"],"abstract_inverted_index":{"<p":[0],"style='text-indent:20px;'>In":[1],"this":[2],"paper,":[3],"we":[4],"evaluate":[5],"two":[6],"deep":[7],"learning":[8,49],"models":[9,50],"which":[10],"integrate":[11],"convolutional":[12],"and":[13,21,69,98,127],"recurrent":[14],"neural":[15],"networks.":[16],"We":[17,57],"implement":[18],"both":[19],"sequential":[20,80,123],"parallel":[22,89,119],"architectures":[23],"for":[24],"fine-grain":[25],"musical":[26],"subgenre":[27],"classification.":[28],"Due":[29],"to":[30,35,53,71],"the":[31,59,79,84,88,93,116,122],"exceptionally":[32],"low":[33,41],"signal":[34],"noise":[36],"ratio":[37],"(SNR)":[38],"of":[39,61,106],"our":[40],"level":[42],"mel-spectrogram":[43],"dataset,":[44],"more":[45],"sensitive":[46],"yet":[47],"robust":[48],"are":[51,114],"required":[52],"generate":[54],"meaningful":[55],"results.":[56],"investigate":[58],"effects":[60],"three":[62],"commonly":[63],"applied":[64],"optimizers,":[65],"dropout,":[66],"batch":[67],"regularization,":[68],"sensitivity":[70],"varying":[72],"initialization":[73],"distributions.":[74],"The":[75],"results":[76,100],"demonstrate":[77],"that":[78],"model":[81,90,120],"specifically":[82],"requires":[83],"RMSprop":[85],"optimizer,":[86],"while":[87],"implemented":[91],"with":[92],"Adam":[94],"optimizer":[95],"yielded":[96],"encouraging":[97],"stable":[99],"achieving":[101],"an":[102],"average":[103],"F1":[104],"score":[105],"<inline-formula><tex-math":[107],"id=\"M1\">\\begin{document}$":[108],"0.63":[109],"$\\end{document}</tex-math></inline-formula>.":[110],"When":[111],"all":[112],"factors":[113],"considered,":[115],"optimized":[117],"hybrid":[118],"outperformed":[121],"in":[124],"classification":[125],"accuracy":[126],"system":[128],"stability.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-23T05:56:39.545243","created_date":"2025-10-10T00:00:00"}
