APOLONN brings us to the real world: learning nonlinear dynamics and fluctuations in nature
- 1 January 1990
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 581-587 vol.1
- https://doi.org/10.1109/ijcnn.1990.137631
Abstract
Recurrent neural networks with arbitrary feedback connections are highly nonlinear dynamical systems exhibiting variegated complex dynamical behavior. The applications of this temporal behavior hold possibilities for information processing. Supervised learning for recurrent networks is studied with emphasis on learning aperiodic motions. APOLONN (adaptive nonlinear pair oscillators with local connections) is used for speech synthesis. The naturalness of a human's voice seems to come from fluctuations in voice source waveforms. The authors trained APOLONN to learn the voice source waveforms, including fluctuations of amplitudes and periodicities. After the learning, APOLONN was able to generate the waveforms with fluctuations. APOLONN can also generate waveforms with modulated amplitudes and frequencies by a simple scaling of the parameters. The results encourage further applications of recurrent networksKeywords
This publication has 15 references indexed in Scilit:
- A learning algorithm to teach spatiotemporal patterns to recurrent neural networksBiological Cybernetics, 1990
- A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksNeural Computation, 1989
- Learning State Space Trajectories in Recurrent Neural NetworksNeural Computation, 1989
- Generalization of back-propagation to recurrent neural networksPhysical Review Letters, 1987
- Review of text-to-speech conversion for EnglishThe Journal of the Acoustical Society of America, 1987
- Absolute Stability of Global Pattern Formation and Parallel Memory Storage by Competitive Neural NetworksPublished by Elsevier ,1987
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982
- Software for a cascade/parallel formant synthesizerThe Journal of the Acoustical Society of America, 1980