《现代电影技术》｜熊晓钰等：基于多粒度注意力Transformer的电影音乐生成研究

1 引言

2 相关研究

3 本文方法

4 实验结果及分析

5 结语

参考文献

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[1] 陈吉尚, 哈里旦木·阿布都克里木, 梁蕴泽, 等. 深度学习在符号音乐生成中的应用研究综述[J]. 计算机工程与应用, 2023, 59(09): 27⁃45.

[2] Roberts A, Engel J, Raffel C, et al. A hierarchical latent vector model for learning long⁃term structure in music[C]//International conference on machine learning. PMLR, 2018: 4364⁃4373.

[3] Brunner G, Konrad A, Wang Y, et al. MIDI⁃VAE: Modeling dynamics and instrumentation of music with applications to style transfer[C]//ISMIR 2019: 343⁃351.

[4] Mogren O. C⁃RNN⁃GAN: Continuous recurrent neural networks with adversarial training[C]//Conference and Workshop on Neural Information Processing Systems, 2016: 1⁃6.

[5] Yang L C, Chou S Y, Yang Y H. MidiNet: A convolutional generative adversarial network for symbolic⁃domain music generation[C]//Proceedings of International Society for Music Information Retrieval Conference, 2017: 324⁃331.

[6] Dong H W, Hsiao W Y, Yang L C, et al. MuseGAN: Multi⁃track sequential generative adversarial networks for symbolic music generation and accompaniment[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018: 34⁃41.

[7] Huang C Z A, Vaswani A, Uszkoreit J, et al. Music Transformer: Generating music with long⁃term structure[C]// International Conference on Learning Representations,2018: 123⁃131.

[8] Huang Y S, Yang Y H. Pop Music Transformer: Beat⁃based modeling and generation of expressive pop piano compositions[C]//Proceedings of the 28th ACM international conference on multimedia, 2020: 1180⁃1188.

[9] Dai Z, Yang Z, Yang Y, et al. Transformer⁃XL: Attentive language models beyond a fixed⁃length context[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 2978⁃2988.

[10] Hsiao W Y, Liu J Y, Yeh Y C, et al. Compound Word Transformer: Learning to compose full⁃song music over dynamic directed hypergraphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 178⁃186.

[11] Katharopoulos A, Vyas A, Pappas N, et al. Transformers are rnns: Fast autoregressive transformers with linear attention[C]//International Conference on Machine Learning, Proceedings of Machine Learning Research, 2020: 5156⁃5165.

[12] Zhang N. Learning adversarial transformer for symbolic music generation[J]. IEEE transactions on neural networks and learning systems, 2020, 34(4): 1754⁃1763.

[13] Muhamed A, Li L, Shi X, et al. Symbolic music generation with transformer⁃gans [C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2021:408⁃417.

[14] Wang L, Zhao Z, Liu H, et al. A review of intelligent music generation systems[J]. Neural Computing and Applications, 2024: 1⁃21.

[15] Mao H H, Shin T, Cottrell G. DeepJ: Style⁃specific music generation [C]//2018 IEEE 12th International Conference on Semantic Computing (ICSC). IEEE, 2018: 377⁃382.

[16] Johnson D D. Generating polyphonic music using tied parallel networks[C]//International conference on evolutionary and biologically inspired music and art. Cham: Springer International Publishing, 2017: 128⁃143.

[17] Wang Z, Wang D, Zhang Y, et al. Learning interpretable representation for controllable polyphonic music generation[C]//Proceedings of the 21st International Society for Music Information Retrieval Conference, 2020: 662–669.

[18] Choi K, Hawthorne C, Simon I, et al. Encoding musical style with transformer autoencoders[C]//International conference on machine learning. PMLR, 2020: 1899⁃1908.

[19] Di S, Jiang Z, Liu S, et al. Video background music generation with controllable music transformer[C]//Proceedings of the 29th ACM International Conference on Multimedia, 2021: 2037⁃2045.

[20] Zhuo L, Wang Z, Wang B, et al. Video background music generation: Dataset, method and evaluation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 15637⁃15647.

[21] Hung H T, Ching J, Doh S, et al. EMOPIA: A multi⁃modal pop piano dataset for emotion recognition and emotion⁃based music generation[C]//Proceedings of the 22nd International Society for Music Information Retrieval Conference, 2021: 318⁃325.

[22] Kang J, Poria S, Herremans D. Video2Music: Suitable music generation from videos using an Affective Multimodal Transformer model[J]. Expert Systems with Applications, 2024, 249: 123640.

[23] Ding Z, Liu X, Zhong G, et al. Steelygan: Semantic unsupervised symbolic music genre transfer[C]//Chinese Conference on Pattern Recognition and Computer Vision, Cham: Springer International Publishing, 2022: 305⁃317.

[24] Wu S L, Yang Y H. MuseMorphose: Full⁃song and fine⁃grained piano music style transfer with one transformer VAE[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31: 1953⁃1967.

[25] Huang H, Wang Y, Li L, et al. Music style transfer with diffusion model[C]// Proceedings of International Computer Music Conference, 2023: 39⁃46.

[26] Wang W, Li X, Jin C, et al. CPS: full⁃song and style⁃conditioned music generation with linear transformer[C]//2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2022: 1⁃6.

[27] Sarmento P, Kumar A, Chen Y H, et al. GTR⁃CTRL: Instrument and genre conditioning for guitar⁃focused music generation with transformers[C]//International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar), Cham: Springer Nature Switzerland, 2023: 260⁃275.

[28] Yu B, Lu P, Wang R, et al. Museformer: Transformer with fine⁃and coarse⁃grained attention for music generation[J]. Advances in Neural Information Processing Systems, 2022, 35: 1376⁃1388.

[29] Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier gans[C]//International conference on machine learning. PMLR, 2017: 2642⁃2651.

[30] Zeng M, Tan X, Wang R, et al. Musicbert: Symbolic music understanding with large⁃scale pre⁃training[C]//Proceedings of Findings of the Association for Computational Linguistics, 2021: 791–800.