Ĭreswell A et al (2018) Generative adversarial networks an overview. Ĭonklin D, Gasser M, Oertl S (2018) Creative chord sequence generation for electronic dance music. Ĭhen JN, Zhang C, Luo JT, Xie JF, Wan Y (2020) Driving maneuvers prediction based autonomous driving control by deep monte carlo tree search. IEEE Trans Comput Intell AI Games 4:1–43. īrowne CB et al (2012) A survey of Monte Carlo tree search methods. Accessed īi CK et al (2019) Evacuation route recommendation using auto-encoder and Markov decision process. Experimental results show that the algorithm proposed here has a better effect on polyphonic music generation than the latest methods.Īgarwal S, Saxena V, Singal V, Aggarwal S (2018) LSTM based music generation with dataset preprocessing and reconstruction techniquesĪrjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. Through the zero-sum game and conditional constraints between generator and discriminator, the model in this study is closer to the unconstrained creation of music, and the growth of music sequence will not affect music coherence. Therefore, this paper proposes a novel polyphonic music creation model, combining the ideas of the Markov decision process (MDP) and Monte Carlo tree search (MCTS) and improving the Wasserstein Generative Adversarial Network (WGAN) theory. As the music sequence increases, the probability of the generator producing the same note will increase, which will destroy the coherence of the music. However, due to the diversity of polyphonic music sequences and the limitations of neural networks, it is difficult to create chords or melodies beyond the training data. In the process of polyphonic music creation, it is important to combine two or more independent melodies through technical treatment.
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