{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:02:46Z","timestamp":1753887766683,"version":"3.41.2"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":271,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Automatic extraction of features from harmonic information of music audio is considered in this paper. Automatically obtaining of relevant information is necessary not just for analysis but also for the commercial issue such as music program of tutoring and generating of lead sheet. Two aspects of harmony are considered, chord and global key, facing the issue of the extraction problem by the algorithm of machine learning. Contribution here is to recognize chords in the music by the feature extraction method (voiced models) that performd better than manually one. The modelling carried out chord sequence, getting from frame\u2010by\u2010frame basis, which is known in recognition of the chord system. Technique of machine learning such the convolutional neural network (CNN) will systematically extract the chord sequence to achieve the superiority context model. Then, traditional classification is used to create the key classifier which is better than others or manually one. Datasets used to evaluate the proposed model show good achievement results compared with existing one.<\/jats:p>","DOI":"10.1155\/2021\/5590996","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:55:57Z","timestamp":1632959757000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Harmonic Classification with Enhancing Music Using Deep Learning Techniques"],"prefix":"10.1155","volume":"2021","author":[{"given":"Wen","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0689-1727","authenticated-orcid":false,"given":"Linlin","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"CholletF. Xception: deep learning with depthwise separable convolutions Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition July 2017 Honolulu HI USA 1251\u20131258 https:\/\/doi.org\/10.1109\/cvpr.2017.195 2-s2.0-85040604274.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_10_2_2","article-title":"Chord classification of an audio signal using artificial neural network","volume":"5","author":"Shrestha R.","year":"2018","journal-title":"International Research Journal of Engineering and Technology (IRJET)"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/taslp.2018.2825440"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1103\/physrevd.99.034503"},{"key":"e_1_2_10_5_2","unstructured":"KorzeniowskiF.andWidmerG. On the futility of learning complex frame-level language models for chord recognition 2017 https:\/\/arxiv.org\/abs\/1702.00178."},{"key":"e_1_2_10_6_2","doi-asserted-by":"crossref","unstructured":"TakahashiN. GoswamiN. andMitsufujiY. Mmdenselstm: an efficient combination of convolutional and recurrent neural networks for audio source separation Proceedings of the 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC) September 2018 Tokyo Japan IEEE 106\u2013110 https:\/\/doi.org\/10.1109\/iwaenc.2018.8521383 2-s2.0-85057373405.","DOI":"10.1109\/IWAENC.2018.8521383"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/09298215.2021.1873392"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"SimonettaF. NtalampirasS. andAvanziniF. Multimodal music information processing and retrieval: survey and future challenges Proceedings of the 2019 International Workshop on Multilayer Music Representation and Processing (MMRP) January 2019 Milan Italy IEEE 10\u201318 https:\/\/doi.org\/10.1109\/mmrp.2019.00012.","DOI":"10.1109\/MMRP.2019.00012"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2895334"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1162\/jocn_a_01022"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2018.2879399"},{"key":"e_1_2_10_12_2","unstructured":"KorzeniowskiandWidmerG. Genre-agnostic key classification with convolutional neural networks Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR) September 2018 Paris France."},{"volume-title":"Computer System for Harmonic Transcription of Jazz Music","year":"2020","author":"Dur\u00e1n G. E.","key":"e_1_2_10_13_2"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13673-018-0158-1"},{"key":"e_1_2_10_15_2","doi-asserted-by":"crossref","unstructured":"HuangH. M. ChenW. K. LiuC. H. andYouS. D. Singing voice detection based on convolutional neural networks Proceedings of the 2018 7th International Symposium on Next Generation Electronics May 2018 Taipei Taiwan https:\/\/doi.org\/10.1109\/isne.2018.8394727 2-s2.0-85050225350.","DOI":"10.1109\/ISNE.2018.8394727"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1162\/jocn_a_01022"},{"key":"e_1_2_10_17_2","unstructured":"McFeeB.andJuan PabloB. Structured training for large-vocabulary chord recognition Proceedings of the 18th International Society for Music Information Retrieval Conference 2017 Suzhou China 188\u2013194."},{"key":"e_1_2_10_18_2","unstructured":"KorzeniowskiF.andWidmerG. Improved chord recognition by combining duration and harmonic language models 2018 https:\/\/arxiv.org\/abs\/1808.05335."},{"key":"e_1_2_10_19_2","unstructured":"DongH.-W.andYangYi-H. Convolutional generative adversarial networks with binary neurons for polyphonic music generation 2018 https:\/\/arxiv.org\/abs\/1804.09399."},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3748141"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8861896"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/jstsp.2019.2908700"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1111\/cdev.12652"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"NiemeyerJ. RottensteinerF. SoergelU. andHeipkeC. Hierarchical higher order crf for the classification of airborne lidar point clouds in urban areas ISPRS\u2014International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences July 2016 Prague Czech Republic 655\u2013662 https:\/\/doi.org\/10.5194\/isprsarchives-xli-b3-655-2016 2-s2.0-84978062504.","DOI":"10.5194\/isprsarchives-XLI-B3-655-2016"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2872625"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2016.12.019"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102013"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/app8010150"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2846566"},{"key":"e_1_2_10_30_2","unstructured":"KorzeniowskiF.andWidmerG. Genre-agnostic key classification with convolutional neural networks Proceedings of the 19th International Society for Music Information Retrieval Conference 2018 Paris France."},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/taslp.2013.2295926"},{"key":"e_1_2_10_32_2","unstructured":"FaraldoA. Jord\u00e0S. andHerreraP. A multi-profile method for key estimation in EDM Proceedings of the AES International Conference on Semantic Audio June 2017 Erlangen Germany."},{"key":"e_1_2_10_33_2","unstructured":"CannamC. MauchM. DaviesM. E. DixonS. LandoneC. NolandK. LevyM. ZanoniM. StowellD. andFigueiraL. A. MIREX 2016 entry: vamp plugins from the centre for digital music 2016 MIREX Santo Domingo Dominican Republic Technical report."}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/5590996.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/5590996.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5590996","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:28:50Z","timestamp":1723242530000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5590996"}},"subtitle":[],"editor":[{"given":"Dan","family":"Selistean","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5590996"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5590996","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"type":"print","value":"1076-2787"},{"type":"electronic","value":"1099-0526"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-02-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5590996"}}