  Following the recent progress in Terahertz (THz) signal generation and
radiation methods, joint THz communications and sensing applications are
shaping the future of wireless systems. Towards this end, THz spectroscopy is
expected to be carried over user equipment devices to identify material and
gaseous components of interest. THz-specific signal processing techniques
should complement this re-surged interest in THz sensing for efficient
utilization of the THz band. In this paper, we present an overview of these
techniques, with an emphasis on signal pre-processing (standard normal variate
normalization, min-max normalization, and Savitzky-Golay filtering), feature
extraction (principal component analysis, partial least squares, t-distributed
stochastic neighbor embedding, and nonnegative matrix factorization), and
classification techniques (support vector machines, k-nearest neighbor,
discriminant analysis, and naive Bayes). We also address the effectiveness of
deep learning techniques by exploring their promising sensing capabilities at
the THz band. Lastly, we investigate the performance and complexity trade-offs
of the studied methods in the context of joint communications and sensing; we
motivate the corresponding use-cases, and we present few future research
directions in the field.
