{"id":"https://openalex.org/W4408575154","doi":"https://doi.org/10.3390/e27030317","title":"Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN\u2013TCN Architecture with Integrated Entropy Regularization and Pooling","display_name":"Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN\u2013TCN Architecture with Integrated Entropy Regularization and Pooling","publication_year":2025,"publication_date":"2025-03-18","ids":{"openalex":"https://openalex.org/W4408575154","doi":"https://doi.org/10.3390/e27030317","pmid":"https://pubmed.ncbi.nlm.nih.gov/40149241"},"language":"en","primary_location":{"id":"doi:10.3390/e27030317","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27030317","pdf_url":"https://www.mdpi.com/1099-4300/27/3/317/pdf?version=1742296880","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/27/3/317/pdf?version=1742296880","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115603090","display_name":"Jiangang Chen","orcid":"https://orcid.org/0000-0003-3050-3592"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]},{"id":"https://openalex.org/I4210095228","display_name":"Xi'an Physical Education University","ror":"https://ror.org/00pt5by23","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210095228"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiangang Chen","raw_affiliation_strings":["College of Sports and Health Sciences, Xi\u2019an Physical Education University, Xi\u2019an 710068, China","School of P. E and Sports, Beijing Normal University, Beijing 100875, China","College of Sports and Health Sciences, Xi'an Physical Education University, Xi'an 710068, China"],"raw_orcid":"https://orcid.org/0000-0003-3050-3592","affiliations":[{"raw_affiliation_string":"College of Sports and Health Sciences, Xi\u2019an Physical Education University, Xi\u2019an 710068, China","institution_ids":["https://openalex.org/I4210095228"]},{"raw_affiliation_string":"School of P. E and Sports, Beijing Normal University, Beijing 100875, China","institution_ids":["https://openalex.org/I25254941"]},{"raw_affiliation_string":"College of Sports and Health Sciences, Xi'an Physical Education University, Xi'an 710068, China","institution_ids":["https://openalex.org/I4210095228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019230010","display_name":"Junbo Han","orcid":"https://orcid.org/0000-0002-5072-4897"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junbo Han","raw_affiliation_strings":["School of P. E and Sports, Beijing Normal University, Beijing 100875, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of P. E and Sports, Beijing Normal University, Beijing 100875, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073577823","display_name":"Pei Su","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pei Su","raw_affiliation_strings":["School of P. E and Sports, Beijing Normal University, Beijing 100875, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of P. E and Sports, Beijing Normal University, Beijing 100875, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108520039","display_name":"G. Tong Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaoquan Zhou","raw_affiliation_strings":["School of P. E and Sports, Beijing Normal University, Beijing 100875, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of P. E and Sports, Beijing Normal University, Beijing 100875, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5115603090"],"corresponding_institution_ids":["https://openalex.org/I25254941","https://openalex.org/I4210095228"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":3.2603,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.90692725,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"27","issue":"3","first_page":"317","last_page":"317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10788","display_name":"Neuroscience and Music Perception","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10788","display_name":"Neuroscience and Music Perception","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9995999932289124,"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":0.996999979019165,"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/pooling","display_name":"Pooling","score":0.7109233140945435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6752233505249023},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.642235279083252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6412529945373535},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6041067838668823},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5730660557746887},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5046604871749878},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4892566204071045},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4815104901790619},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43731653690338135},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.42981356382369995},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41456136107444763},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.4130849838256836},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3368932902812958}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7109233140945435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6752233505249023},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.642235279083252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6412529945373535},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6041067838668823},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5730660557746887},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5046604871749878},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4892566204071045},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4815104901790619},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43731653690338135},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.42981356382369995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41456136107444763},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.4130849838256836},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3368932902812958},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e27030317","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27030317","pdf_url":"https://www.mdpi.com/1099-4300/27/3/317/pdf?version=1742296880","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmid:40149241","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40149241","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:387a4ec0e00f4e61b5bfeba63d72752e","is_oa":true,"landing_page_url":"https://doaj.org/article/387a4ec0e00f4e61b5bfeba63d72752e","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 27, Iss 3, p 317 (2025)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:11941122","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11941122","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e27030317","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e27030317","pdf_url":"https://www.mdpi.com/1099-4300/27/3/317/pdf?version=1742296880","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G7504370683","display_name":"\u57fa\u4e8e\u8fd0\u52a8\u7b5b\u67e5\u7684\u5927\u5b66\u751f\u6162\u75c5\u98ce\u9669\u8fd0\u52a8\u5e72\u9884\u4f53\u533b\u878d\u5408\u7b56\u7565\u6784\u5efa\u4e0e\u5b9e\u8bc1\u7814\u7a76","funder_award_id":"71874017","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":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4408575154.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1986477310","https://openalex.org/W2019360207","https://openalex.org/W2036571109","https://openalex.org/W2046998346","https://openalex.org/W2062385550","https://openalex.org/W2083116372","https://openalex.org/W2094644168","https://openalex.org/W2110743726","https://openalex.org/W2137560425","https://openalex.org/W2145494108","https://openalex.org/W2194601815","https://openalex.org/W2419636006","https://openalex.org/W2782008129","https://openalex.org/W2803098273","https://openalex.org/W2944458161","https://openalex.org/W2964184826","https://openalex.org/W2987463460","https://openalex.org/W3011132328","https://openalex.org/W3044476465","https://openalex.org/W3202837723","https://openalex.org/W4205213167","https://openalex.org/W4205622176","https://openalex.org/W4225581311","https://openalex.org/W4280572954","https://openalex.org/W4304203549","https://openalex.org/W4309890417","https://openalex.org/W4311811376","https://openalex.org/W4372334073","https://openalex.org/W4385336226","https://openalex.org/W4390064594","https://openalex.org/W4391749456","https://openalex.org/W4392904016","https://openalex.org/W4395055898","https://openalex.org/W4398775603","https://openalex.org/W6682106578"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W3022252430","https://openalex.org/W4390975304","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W3211292372","https://openalex.org/W2994927414"],"abstract_inverted_index":{"Groove,":[0],"a":[1,8,38,73,80,151],"complex":[2],"aspect":[3],"of":[4,107,130,158],"music":[5,165],"perception,":[6],"plays":[7],"crucial":[9],"role":[10],"in":[11,102,121,164],"eliciting":[12],"emotional":[13],"and":[14,22,59,78,110,132,142,168,181],"physical":[15],"responses":[16],"from":[17],"listeners.":[18],"However,":[19],"accurately":[20],"quantifying":[21],"predicting":[23],"groove":[24,42,192],"remains":[25],"challenging":[26],"due":[27],"to":[28,82,118,186],"its":[29],"intricate":[30],"acoustic":[31],"features.":[32],"To":[33],"address":[34],"this,":[35],"we":[36],"propose":[37],"novel":[39],"framework":[40,97],"for":[41,75,154],"rating":[43],"that":[44,94],"integrates":[45],"Convolutional":[46,52],"Neural":[47],"Networks":[48,53],"(CNNs)":[49],"with":[50,114,161],"Temporal":[51],"(TCNs),":[54],"enhanced":[55],"by":[56,72,79],"entropy":[57,108],"regularization":[58,111],"entropy-pooling":[60],"techniques.":[61],"Our":[62,124],"approach":[63],"processes":[64],"audio":[65],"files":[66],"into":[67],"Mel-spectrograms,":[68],"which":[69],"are":[70],"analyzed":[71],"CNN":[74,131],"feature":[76],"extraction":[77],"TCN":[81],"capture":[83],"long-range":[84],"temporal":[85],"dependencies,":[86],"enabling":[87],"precise":[88],"groove-level":[89],"prediction.":[90],"Experimental":[91],"results":[92],"show":[93],"our":[95],"CNN\u2013TCN":[96],"significantly":[98],"outperforms":[99],"benchmark":[100],"methods":[101],"predictive":[103],"accuracy.":[104],"The":[105],"integration":[106],"pooling":[109],"is":[112],"critical,":[113],"their":[115],"omission":[116],"leading":[117],"notable":[119],"reductions":[120],"R2":[122],"values.":[123],"method":[125],"also":[126],"surpasses":[127],"the":[128,155,176,189],"performance":[129],"other":[133],"machine-learning":[134,184],"models,":[135],"including":[136],"long":[137],"short-term":[138],"memory":[139],"(LSTM)":[140],"networks":[141],"support":[143],"vector":[144],"machine":[145],"(SVM)":[146],"variants.":[147],"This":[148],"study":[149],"establishes":[150],"strong":[152],"foundation":[153],"automated":[156],"assessment":[157],"musical":[159],"groove,":[160],"potential":[162],"applications":[163],"education,":[166],"therapy,":[167],"composition.":[169],"Future":[170],"research":[171],"will":[172],"focus":[173],"on":[174],"expanding":[175],"dataset,":[177],"enhancing":[178],"model":[179],"generalization,":[180],"exploring":[182],"additional":[183],"techniques":[185],"further":[187],"elucidate":[188],"factors":[190],"influencing":[191],"perception.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}
