  Sensor drift is a major problem in chemical sensors that requires addressing
for reliable and accurate detection of chemical analytes. In this paper, we
develop a causal convolutional neural network (CNN) with a Discrete Cosine
Transform (DCT) layer to estimate the drift signal. In the DCT module, we apply
soft-thresholding nonlinearity in the transform domain to denoise the data and
obtain a sparse representation of the drift signal. The soft-threshold values
are learned during training. Our results show that DCT layer-based CNNs are
able to produce a slowly varying baseline drift signal. We train the CNN on
synthetic data and test it on real chemical sensor data. Our results show that
we can have an accurate and smooth drift estimate even when the observed sensor
signal is very noisy.
