publication . Preprint . 2016

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Mogren, Olof;
Open Access English
  • Published: 29 Nov 2016
Abstract
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Subjects
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Learning
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Soumith Chintala Alec Radford, Luke Metz. Unsupervised representation learning with deep convolutional generative adversarial networks. In International Conference on Learning Representations, 2016.

Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on, 5(2):157-166, 1994. [OpenAIRE]

Emily L Denton, Soumith Chintala, Rob Fergus, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pages 1486-1494, 2015. [OpenAIRE]

Douglas Eck and Juergen Schmidhuber. Finding temporal structure in music: Blues improvisation with lstm recurrent networks. In Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on, pages 747-756. IEEE, 2002.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672-2680, 2014.

Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

Sepp Hochreiter. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02): 107-116, 1998.

Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110, 2016. [OpenAIRE]

Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernocky`, and Sanjeev Khudanpur. Recurrent neural network based language model. In Interspeech, volume 2, page 3, 2010.

Pascal Vincent Nicolas Boulanger-Lewandowski, Yoshua Bengio. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the 29th International Conference on Machine Learning (ICML), page 1159-1166, 2012.

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2226-2234, 2016.

Jürgen Schmidhuber and Sepp Hochreiter. Long short-term memory. Neural computation, 7(8): 1735-1780, 1997.

Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104-3112, 2014.

Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. arXiv preprint arXiv:1609.05473, 2016.

Abstract
Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Subjects
free text keywords: Computer Science - Artificial Intelligence, Computer Science - Learning
Related Organizations
Download from

Soumith Chintala Alec Radford, Luke Metz. Unsupervised representation learning with deep convolutional generative adversarial networks. In International Conference on Learning Representations, 2016.

Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on, 5(2):157-166, 1994. [OpenAIRE]

Emily L Denton, Soumith Chintala, Rob Fergus, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pages 1486-1494, 2015. [OpenAIRE]

Douglas Eck and Juergen Schmidhuber. Finding temporal structure in music: Blues improvisation with lstm recurrent networks. In Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on, pages 747-756. IEEE, 2002.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pages 2672-2680, 2014.

Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.

Sepp Hochreiter. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02): 107-116, 1998.

Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic. Generating images with recurrent adversarial networks. arXiv preprint arXiv:1602.05110, 2016. [OpenAIRE]

Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernocky`, and Sanjeev Khudanpur. Recurrent neural network based language model. In Interspeech, volume 2, page 3, 2010.

Pascal Vincent Nicolas Boulanger-Lewandowski, Yoshua Bengio. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proceedings of the 29th International Conference on Machine Learning (ICML), page 1159-1166, 2012.

Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems, pages 2226-2234, 2016.

Jürgen Schmidhuber and Sepp Hochreiter. Long short-term memory. Neural computation, 7(8): 1735-1780, 1997.

Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104-3112, 2014.

Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. Seqgan: Sequence generative adversarial nets with policy gradient. arXiv preprint arXiv:1609.05473, 2016.

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