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Knowl. Discov. Data"],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music are not well addressed, such as chord progression, rhythm pattern, and musical style. In order to tackle the problems and ensure the harmony of multi-track music, in this article, we propose an end-to-end melody and arrangement generation framework to generate a melody track with several accompany tracks played by some different instruments. To be specific, we first develop a novel\n            <jats:italic>Chord based Rhythm and Melody Cross-Generation Model<\/jats:italic>\n            to generate melody with a chord progression. Then, we propose a\n            <jats:italic>Multi-Instrument Co-Arrangement Model<\/jats:italic>\n            based on multi-task learning for multi-track music arrangement. Furthermore, to control the musical style of arrangement, we design a\n            <jats:italic>Multi-Style Multi-Instrument Co-Arrangement Model<\/jats:italic>\n            to learn the musical style with adversarial training. Therefore, we can not only maintain the harmony of the generated music but also control the musical style for better utilization. Extensive experiments on a real-world dataset demonstrate the superiority and effectiveness of our proposed models.\n          <\/jats:p>","DOI":"10.1145\/3374915","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T21:19:53Z","timestamp":1594070393000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Pop Music Generation"],"prefix":"10.1145","volume":"14","author":[{"given":"Hongyuan","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Nicholas Jing","family":"Yuan","sequence":"additional","affiliation":[{"name":"Huawei Cloud8AI, Hangzhou, Zhejiang, China"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]},{"given":"Guang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Microsoft, Suzhou, China"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}]}],"member":"320","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Binder Ready Version: Applications Version","author":"Anton Howard"},{"key":"e_1_2_1_2_1","volume-title":"3rd International Conference on Learning Representations (ICLR'15)","author":"Bahdanau Dzmitry","year":"2015"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.91.3.1059"},{"key":"e_1_2_1_4_1","volume-title":"Deep Listeners: Music, Emotion, and Trancing.","author":"Becker Judith O.","year":"2004"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2652481"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_2_1_7_1","volume-title":"A unit selection methodology for music generation using deep neural networks. arXiv preprint arXiv:1612.03789","author":"Bretan Mason","year":"2016"},{"key":"e_1_2_1_8_1","volume-title":"Deep learning techniques for music generation-a survey. arXiv preprint arXiv:1709.01620","author":"Briot Jean-Pierre","year":"2017"},{"key":"e_1_2_1_9_1","volume-title":"19th International Society for Music Information Retrieval Conference (ISMIR'18)","author":"Brunner Gino","year":"2018"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44812-8_18"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/09298215.2011.576318"},{"key":"e_1_2_1_13_1","volume-title":"Song from pi: A musically plausible network for pop music generation. arXiv preprint arXiv:1611.03477","author":"Chu Hang","year":"2016"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the AISB 2003 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences. 30--35","author":"Conklin Darrell","year":"2003"},{"key":"e_1_2_1_16_1","volume-title":"Music style transfer issues: A position paper. arXiv preprint arXiv:1803.06841","author":"Dai Shuqi","year":"2018"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1166"},{"key":"e_1_2_1_18_1","volume-title":"Thirty-Second AAAI Conference on Artificial Intelligence.","author":"Dong Hao-Wen","year":"2018"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0959-440X(96)80056-X"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the International Association for the Study of Popular Music.","author":"Fabbri Franco","year":"2007"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623675"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the Advances in Neural Information Processing Systems. 2672--2680","author":"Goodfellow Ian","year":"2014"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning","volume":"70","author":"Hadjeres Ga\u00ebtan","year":"2017"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1178723.1178727"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1206"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 42nd Audio Engineering Society Conference. 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