{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:20:39Z","timestamp":1778347239587,"version":"3.51.4"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":248,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of music\u2010style classification. This paper proposes a novel method based on music extraction and deep neural networks to address the problem of low accuracy in traditional methods. The music style classification algorithm extracts two types of features as classification characteristics for music styles: timbre and melody features. Because the classification method based on a convolutional neural network ignores the audio\u2019s timing. As a result, we proposed a music classification module based on the one\u2010dimensional convolution of a recurring neuronal network, which we combined with single\u2010dimensional convolution and a two\u2010way, recurrent neural network. To better represent the music style properties, different weights are applied to the output. The GTZAN data set was also subjected to comparison and ablation experiments. The test results outperformed a number of other well\u2010known methods, and the rating performance was competitive.<\/jats:p>","DOI":"10.1155\/2021\/9298654","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T23:50:20Z","timestamp":1630972220000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7825-7170","authenticated-orcid":false,"given":"Kedong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2017.00494"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1525\/mp.2013.30.5.517"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.3758\/BF03193159"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2018.0731"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"GuimaraesP. FroesJ. CostaD. andde FreitasL. A. A comparison of identification methods of Brazilian music styles by lyrics Proceedings of the Fourth Widening Natural Language Processing Workshop 2020 July Seattle USA 61\u201363.","DOI":"10.18653\/v1\/2020.winlp-1.16"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1163\/9789004335288_016"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/mts\/mtaa003"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/1029864918757595"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/MMUL.2018.023121167"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.5815\/ijmecs.2017.01.03"},{"key":"e_1_2_8_11_2","first-page":"50","article-title":"Multimedia technologies as a means of training athletes in student basketball","volume":"4","author":"Kozina Z. 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Music genre classification: a comparative study between deep-learning and traditional machine learning approaches Sixth International Congress on Information and Communication Technology (6th ICICT) 2021 London 1\u20138.","DOI":"10.1007\/978-981-16-2102-4_22"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.52810\/TPRIS.2021.100050"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCPMT.2021.3102891"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.52810\/TIOT.2021.100035"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2963873"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSA.2002.800560"},{"key":"e_1_2_8_25_2","doi-asserted-by":"crossref","unstructured":"XuC. MaddageN. C. ShaoX. CaoF. andTianQ. Musical genre classification using support vector machines 5 2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP \u203203) 2003 April Hong Kong China V\u2013429 https:\/\/doi.org\/10.1109\/ICASSP.2003.1199998.","DOI":"10.1109\/ICASSP.2003.1199998"},{"key":"e_1_2_8_26_2","doi-asserted-by":"crossref","unstructured":"LiT. OgiharaM. andLiQ. A comparative study on content-based music genre classification Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval 2003 July Toronto Canada 282\u2013289 https:\/\/doi.org\/10.1145\/860435.860487.","DOI":"10.1145\/860484.860487"},{"key":"e_1_2_8_27_2","unstructured":"PanagakisI. BenetosE. andKotropoulosC. Music genre classification: a multilinear approach International Symposium Music Information Retrieval 2008 Philadelphia USA 583\u2013588."},{"key":"e_1_2_8_28_2","first-page":"1","article-title":"Mel-frequency cepstral coefficient features based on standard deviation and principal component analysis for language identification systems","author":"Albadr M. A. 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