![]() Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville.In Advances in neural information processing systems. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.Adam Roberts Dan Abolafia Elliot Waite, Douglas Eck.Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale, Vol. A first look at music composition using lstm recurrent neural networks. In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI). MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment. Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang.Chord sequence generation with semiotic patterns. ![]() In workshop on International Conference on Learning Representations, 2017. Song From PI: A Musically Plausible Network for Pop Music Generation. Hang Chu, Raquel Urtasun, and Sanja Fidler.In Advances in Neural Information Processing Systems. Signature verification using a" siamese" time delay neural network. Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard S"ackinger, and Roopak Shah.In Proceedings of the 29th International Coference on International Conference on Machine Learning. Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription. Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent.Proceedings of the National Academy of Sciences, Vol. Martin Arjovsky, Soumith Chintala, and Léon Bottou.How many music centers are in the brain? Annals of the New York Academy of Sciences, Vol. I Elaine Allen and Christopher A Seaman.We provide empirical validations by generating the music samples under various scenarios. Specifically, the novelty critic is implemented by Siamese neural networks with temporal alignment using dynamic time warping. We implement the proposed framework using three supervised CNNs with one for generator, one for musicality critic and one for novelty critic on the time-pitch feature space. A new model called novelty game is presented to maximize the minimal distance between the machine-composed music sample and any human-composed music sample in the novelty space, where all well-known human composed music products are far from each other. With the same generator, two adversarial nets alternately optimize the musicality and novelty of the machine-composed music. To deliver both melodious and refreshing music, this paper proposes the Musicality-Novelty Generative Adversarial Nets for algorithmic composition. Algorithmic composition, which enables computer to generate music like human composers, has lasting charm because it intends to approximate artistic creation, most mysterious part of human intelligence.
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