Reviews: Learning filter widths of spectral decompositions with wavelets

In the context of time series classification, this paper introduces the Wavelet Deconvolution layer, which is able to learn the filters width of a neural network-based classifier, which is usually tuned by hand through hyper-parameters grid search. Experimental studies on time series are presented, including a pilot study using artificial data, a phone recognition study on TIMIT and a time series classification study on the Haptic UCR dataset. It is shown that the proposed method leads to similar of better results than state-of-the-art systems. Quality: The paper is correct to my understanding. The experiments are convincing and clearly show the benefit of the proposed approach.