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Exploiting the latent space of deep AutoEncoders for the identification of signal pulses in noisy time-series
We propose a data-driven procedure, based on convolutional variational autoencoders, to identify the presence of signal pulses in long time-series. The dataset consists of synthetic waveforms, each composed of non-gaussian noise and a log-normal shaped signal of variable intensity, with a length of
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