APOLONN brings us to the real world: learning nonlinear dynamics and fluctuations in nature

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 networks