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We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling.
https://www.midi.org/specifications
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Colin Raffel, ‘Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching’, PhD thesis, Columbia University
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Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola, ‘Style transfer from non-parallel text by cross-alignment’, in Advances in neural information processing systems
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Li-Chia Yang, Szu-Yu Chou, and Yi-Hsuan Yang, ‘Midinet: A convolutional generative adversarial network for symbolic-domain music generation’, Proc. ISMIR
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Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu, ‘Seqgan: Sequence generative adversarial nets with policy gradient’, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pages 2852–2858, 2017
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Martin Arjovsky, Soumith Chintala, and Léon Bottou, ‘Wasserstein gan’, arXiv:1701.07875
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Gino Brunner, Andres Konrad, Yuyi Wang, and Roger Wattenhofer, ‘Midi-vae: Modeling dynamics and instrumentation of music with applications to style transfer’, in ISMIR
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Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang, ‘Musegan: Multitrack sequential generative adversarial networks for symbolic music generation and accompaniment’, Proceedings of the 32nd AAAI Conference on Artificial Intelligence
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Hao-Wen Dong, Wen-Yi Hsiao, and Yi-Hsuan Yang, ‘Pypianoroll: Open source python package for handling multitrack pianorolls’, ISMIR Late-Breaking Demos Session
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Yongcheng Jing, Yezhou Yang, Zunlei Feng, Jingwen Ye, Yizhou Yu, and Mingli Song, ‘Neural style transfer: A review’, IEEE transactions on visualization and computer graphics
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