Trainable Wavelet Neural Network for Non-Stationary Signals. (arXiv:2205.03355v1 [cs.LG])

Web: http://arxiv.org/abs/2205.03355

This work introduces a wavelet neural network to learn a filter-bank
specialized to fit non-stationary signals and improve interpretability and
performance for digital signal processing. The network uses a wavelet transform
as the first layer of a neural network where the convolution is a parameterized
function of the complex Morlet wavelet. Experimental results, on both
simplified data and atmospheric gravity waves, show the network is quick to
converge, generalizes well on noisy data, and outperforms standard network
architectures.

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