AbstractNet: A Generative Model for High Density Inputs

  • Boris MusaraisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


This paper introduces AbstractNet, a generative model for high density inputs. The model suggests a method that uses unsupervised learning to generate feature maps. The model drastically improves the performances of raw audio generation by reducing the required amount of input data and computing power necessary to achieve a similar result when compared to the state of the art.


Unsupervised learning Generative model Audio Auto-Encoder LSTM RNN Deep neural networks Data compression 



I want to thank Alain Lioret from Université Paris 8, Aurélien Schlossman from Ariane Group, Nicolas Vidal, Martin Tricaud and everyone at Ecole Superieure De Génie Informatique (ESGI). I would also like to thank all the people who believed in this project.


  1. 1.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  2. 2.
    Nayebi, A., Vitelli, M.: GRUV: Algorithmic Music Generation using Recurrent Neural Networks (2015)Google Scholar
  3. 3.
    van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kavukcuoglu, K.: Wavenet: A generative model for raw audio. CoRR abs/1609.03499 (2016)Google Scholar
  4. 4.
    Dutilleux, P.: An implementation of the “algorithme à trous” to compute the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets. Inverse Problems and Theoretical Imaging, pp. 298–304. Springer, Heidelberg (1989). Scholar
  5. 5.
    Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets. Inverse Problems and Theoretical Imaging, pp. 286–297. Springer, Heidelberg (1990). Scholar
  6. 6.
    Akaike, H.: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21(1), 243–247 (1969)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Aistats, vol. 9, pp. 249–256, May 2010Google Scholar
  8. 8.
    Montana, D.J., Davis, L.: Training feedforward neural networks using genetic algorithms. In: IJCAI, vol. 89, pp. 762–767, August 1989Google Scholar
  9. 9.
    Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., Dean, J.: Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)
  10. 10.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by Back-Propagating Errors (1988)Google Scholar
  12. 12.
    Adali, T., Liu, X., Sonmez, M.K.: Conditional distribution learning with neural networks and its application to channel equalization. IEEE Trans. Sig. Process. 45(4), 1051–1064 (1997)CrossRefGoogle Scholar
  13. 13.
    Cox, G.: On the relationship between entropy and meaning in music: an exploration with recurrent neural networks. In: Proceedings of the Annual Meeting of the Cognitive Science Society (2010)Google Scholar
  14. 14.
    Ahalt, S.C., Krishnamurthy, A.K., Chen, P., Melton, D.E.: Competitive learning algorithms for vector quantization. Neural Netw. 3(3), 277–290 (1990)CrossRefGoogle Scholar
  15. 15.
    Taylor, P.: Text-To-Speech Synthesis. Cambridge university press, Cambridge (2009)Google Scholar
  16. 16.
    Ze, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 7962–7966. IEEE, May 2013Google Scholar
  17. 17.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Deng, L.Y.: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning (2006)Google Scholar
  19. 19.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  20. 20.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
  21. 21.
    Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300 (2015)

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.ESGIParisFrance

Personalised recommendations