Coarse-To-Fine Framework For Music Generation via Generative Adversarial Networks

Author:

Ma Dan1,Liu Bin2,Qiao Xiyu3,Cao Danni2,Yin Guosheng4

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. Center of Statistics Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, China

3. College of Science and Engineering, The university of Edinburgh Edinburgh, UK

4. Department of Statistics and Actuarial Science, The University of Hong Kong

Publisher

ACM

Reference20 articles.

1. Dihydrogen Generation from Amine/Boranes: Synthesis, FT-ICR, and Computational Studies

2. Hang Chu Raquel Urtasun and Sanja Fidler. 2016. Song from PI: A musicallyplausible network for pop music generation.arXiv preprint arXiv:1611.03477(2016). Hang Chu Raquel Urtasun and Sanja Fidler. 2016. Song from PI: A musicallyplausible network for pop music generation.arXiv preprint arXiv:1611.03477(2016).

3. Douglas Eck and Juergen Schmidhuber. 2002.A First Look at Music Composi-tion using LSTM Recurrent Neural Networks. Istituto Dalle Molle Di Studi SullIntelligenza Artificiale. Douglas Eck and Juergen Schmidhuber. 2002.A First Look at Music Composi-tion using LSTM Recurrent Neural Networks. Istituto Dalle Molle Di Studi SullIntelligenza Artificiale.

4. Jon Gauthier. 2014. Conditional generative adversarial nets for convolutionalface generation. Class Project for Stanford CS231N: Convolutional Neural Networksfor Visual Recognition Winter semester2014 5 (2014) 2. Jon Gauthier. 2014. Conditional generative adversarial nets for convolutionalface generation. Class Project for Stanford CS231N: Convolutional Neural Networksfor Visual Recognition Winter semester2014 5 (2014) 2.

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3. A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends;Expert Systems with Applications;2022-12

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