An application of Beta wavelet networks to synthesize pass-high and pass-low wavelet filters is investigated in this work. A Beta wavelet network is constructed using a parametric function called Beta function in order to resolve some nonlinear approximation problem. We combine the filter design theory with wavelet network approximation to synthesize perfect filter reconstruction. The order fil...

In this project, we focus on building a novel parametric TTS system. Our model is based on WaveNet(Oord et al, 2016), a deep neural network introduced by DeepMind in late 2016 for generating raw audio waveforms. It is fully probabilistic, with the predictive distribution for each audio sample conditioned on all previous samples. The model introduces the idea of convolutional layer into TTS task...

The prediction of time series data applied to the energy sector (prediction renewable production, forecasting prosumers’ consumption/generation, forecast country-level consumption, etc.) has numerous useful applications. Nevertheless, complexity and non-linear behaviour associated with such kind systems hinder development accurate algorithms. In a context, this paper investigates use state-of-a...

This article provides insights into the use of artificial neural networks (ANNs) and convolutional (CNNs) as tools for forecasting economic time series, where uncertainty refers to incomplete information about future. To improve ability CNN architectures capture long-term dependencies in input sequence we used WaveNet models which dilate convolutions with skip connections sequence. The residual...

Sequence learning is attracting more and more attention both in industry and academic world with the wide usage of RNN and LSTM neural network architecture. Early this year, Google Brain team open sourced a research project named Magenta, which tries to provide a platform for musicians, artists and programmers to create their music and art works using machine intelligence. Several months later,...