政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136853

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136853

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113313/144292 (79%)
造访人次 : 50949227 在线人数 : 933

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/136853

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136853

题名:	基於 GRU 生成音樂 The Application of GRU to Generate Music
作者:	賴晨心 Lai, Chen-Hsin
贡献者:	蔡瑞煌韓志翔 Tsaih, Rua-Huan Han, Tzu-Shian 賴晨心 Lai, Chen-Hsin
关键词:	人工智慧音樂生成 Neural networks GRU Music generation
日期:	2021
上传时间:	2021-09-02 15:59:35 (UTC+8)
摘要:	儘管從直覺上講，透過適當安排好的輸入/輸出變數表示和 GRU 模型（Cho 等人，2014a ）的超參數能夠學習巴赫和斯卡拉蒂鋼琴音樂來生成音樂，但文獻上仍沒有這樣的學術和實際試驗。本研究接受此挑戰，並設計相關實驗來了解是否能利用 GRU 模型學習巴赫和斯卡拉蒂鋼琴音樂後生成音樂。本研究採用基於節拍的事件數據表示（Huang 和 Yang，2020），並使用不同的輸入/輸出變數表示進行實驗。而在推理階段，我們將 GRU 模型的每個輸出序列連接起來並轉換回 MIDI 文件，從而生成音樂。實驗結果顯示了應用 GRU 生成音樂的正面性。 Although, intuitively, a proper arrangement of input/output presentation and hyperparameters of the state-of-the-art GRU model (Cho et al., 2014a) is capable of learning Bach and Scarlatti piano music and generating the music, there are no such academic and practical trials. This study addresses the challenge of generating music from learning Bach and Scarlatti piano music through the GRU model. This study employs the beat-based event data representation (Huang and Yang, 2020) and conducts an experiment with different input/output representations. As to the inferencing stage, each output sequence of the GRU model is concatenated and transformed back into a MIDI file, so that the music is generated. The experiment shows a positive result regarding the application of GRU to generate music.
參考文獻:	Böck, Sebastian, Florian Krebs, and Gerhard Widmer. (2016). Joint beat and downbeat tracking with recurrent neural networks. Procedure of the 17th Int. Society for Music Information Retrieval Conference (ISMIR), 2016, pp. 255–261. https://archives.ismir.net/ismir2016/paper/000186.pdf. Briot, Jean-Pierre, Gaëtan Hadjeres, and François-David Pachet. (2019). Deep Learning Techniques for Music Generation – A Survey. arXiv:1709.01620 [cs.SD]. https://arxiv.org/abs/1709.01620. Cho, Kyunghyun, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. (2014a). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. arXiv:1406.1078 [cs.CL]. https://arxiv.org/abs/1406.1078. Cho, Kyunghyun, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. (2014b). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv:1409.1259 [cs.CL]. https://arxiv.org/abs/1409.1259. Chollet, François. (2015). Keras. https://github.com/ fchollet/keras. Chung, Junyoung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv:1412.3555 [cs.NE]. https://arxiv.org/abs/1412.3555. Conklin, Darrell. (2003). Music generation from statistical models. Journal of New Music Research, vol. 45, 2016, issue 2, pp. 160 - 183. https://www.tandfonline.com/doi/abs/10.1080/09298215.2016.1173708?journalCode=nnmr20. Cooper, Grosvenor W, Grosvenor Cooper, and Leonard B Meyer. (1963). The Rhythmic Structure of Music. University of Chicago Press. . Cornelisa, Olmo, Micheline Lesaffreb, Dirk Moelantsb, and Marc Leman. (2010). Access to Ethnic Music: Advances and Perspectives in Content-based Music Information Retrieval. Signal Processing, vol. 90, no. 4, pp. 1008–1031. https://www.sciencedirect.com/science/article/abs/pii/S0165168409002874?casa_token=si0RB_X80SQAAAAA:poo0HRgdCMLFcX9LrLen9k1zeT6kmDIdHOd_D13iFicSfKbMTUZnQ23ZrJoIM48CQjXaHfJOtMVx. Dozat, Timothy. (2016). Incorporating Nesterov Momentum into Adam. ICLR Workshop, (1):2013–2016. https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ. Hadjeres, Gaëtan and François Pachet. (2016). DeepBach: a Steerable Model for Bach chorales generation. arXiv preprint arXiv:1612.01010 (2016). https://arxiv.org/abs/1612.01010. Hassan, Hasminda, Zunairah Haji Murat, Valerie Ross, and Norlida Buniyamin. (2012). A preliminary study on the effects of music on human brainwaves. International Conference on Control, Automation and Information Sciences (ICCAIS), Saigon, Vietnam, 2012, pp. 176-180. https://ieeexplore.ieee.org/document/6466581/citations#citations. Hawthorne, Curtis, Anna Huang, Daphne Ippolito, and Douglas Eck. (2018). Transformer-NADE for Piano Performances. NIPS 2nd Workshop on Machine Learning for Creativity and Design. https://www.semanticscholar.org/paper/Transformer-NADE-for-Piano-Performances-Hawthorne/96dd86198c5be28fe91b67f8d49996033fa58bf6. Hochreiter, Sepp and Jürgen Schmidhuber. (1997). Long Short-Term Memory. Neural Computation Vol. 9, Issue 8, November 15, 1997, pp.1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735. Hsiao, Wen-Yi. (2019). miditoolkit. https://libraries.io/pypi/miditoolkit. Huang, Yu-Siang and Yi-Hsuan Yang. (2020). Pop Music Transformer- Beat-based Modeling and Generation of Expressive Pop Piano Compositions. arXiv:2002.00212 [cs.SD]. https://arxiv.org/abs/2002.00212. Kingma, Diederik P and Jimmy Ba. (2017). ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION. arXiv:1412.6980 [cs.LG]. https://arxiv.org/abs/1412.6980. Kingma, Diederik P and Jimmy Lei Ba. (2015). Adam: a Method for Stochastic Optimization. International Conference on Learning Representations, pages 1–13. https://arxiv.org/abs/1412.6980. Kong, Qiuqiang, Bochen Li, Jitong Chen, and Yuxuan Wang. (2020). GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music. arXiv:2010.07061 [cs.IR]. https://arxiv.org/pdf/2010.07061.pdf. Kong, Qiuqiang, Yin Cao, Turab Iqbal, Yong Xu, Wenwu Wang, and Mark D. Plumbley. (2019). Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems. arXiv:1904.03476 [cs.SD]. https://arxiv.org/abs/1904.03476. Kong, Qiuqiang, Bochen Li, Xuchen Song, Yuan Wan, and Yuxuan Wang. (2020). High-resolution Piano Transcription with Pedals by Regressing Onsets and Offsets Times. arXiv:2010.01815 [cs.SD]. https://arxiv.org/abs/2010.01815. Laitz, Steven. (2008). The Complete Musician (2 ed.). New York: Oxford University Press, Inc. pp. 96. MIDI Manufacturers Association (MMA). (14/04/2017). MIDI Specifications. https://www.midi.org/specifications. Mikolov, Tomas, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc`Aurelio Ranzato. (2015). Learning Longer Memory in Recurrent Neural Networks. arXiv:1412.7753 [cs.NE]. https://arxiv.org/abs/1412.7753. Nesterov, Yurii. (1983). A method for unconstrained convex minimization problem with the rate of convergence o(1/k2). Doklady ANSSSR (translated as Soviet.Math.Docl.), 269:543–547.. https://www.semanticscholar.org/paper/A-method-for-unconstrained-convex-minimization-with-Nesterov/ed910d96802212c9e45d956adaa27d915f5d7469. Olah, Christopher. (August 27, 2015). Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/. Oore, Sageev, Ian Simon, Sander Dieleman, Douglas Eck, and Karen Simonyan. (2018). This Time with Feeling: Learning Expressive Musical Performance. arXiv preprint arXiv:1808.03715. https://arxiv.org/abs/1808.03715. Qian, Ning. (1999). On the momentum term in gradient descent learning algorithms. Neural networks : the official journal of the International Neural Network Society, 12(1):145–151. https://www.sciencedirect.com/science/article/abs/pii/S0893608098001166. Turley, L.W and Ronald E Milliman. (2000). Atmospheric effects on shopping behaviour: a review of the experimental evidence. Journal of Business Research, Vol. 49, No. 2, pp. 193-211. https://www.sciencedirect.com/science/article/abs/pii/S0148296399000107. Tze, Peter, and Ming Chou. (2010). Attention drainage effect: How background music effects concentration in Taiwanese college students. Journal of the Scholarship of Teaching and Learning, Vol. 10, No. 1, January 2010, pp. 36 – 46. https://eric.ed.gov/?id=EJ882124.
描述:	碩士國立政治大學資訊管理學系 108356034
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0108356034
数据类型:	thesis
DOI:	10.6814/NCCU202101416
显示于类别:	[資訊管理學系] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
603401.pdf	10422Kb	Adobe PDF2	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈