Signal decomposition (SD) approaches aim to decompose non-stationary signals
into their constituent amplitude- and frequency-modulated components. This
represents an important preprocessing step in many practical signal processing
pipelines, providing useful knowledge and insight into the data and relevant
underlying system(s) while also facilitating tasks such as noise or artefact
removal and feature extraction. The popular SD methods are mostly data-driven,
striving to obtain inherent well-behaved signal components without making many
prior assumptions on input data. Among those methods include empirical mode
decomposition (EMD) and variants, variational mode decomposition (VMD) and
variants, synchrosqueezed transform (SST) and variants and sliding singular
spectrum analysis (SSA). With the increasing popularity and utility of these
methods in wide-ranging application, it is imperative to gain a better
understanding and insight into the operation of these algorithms, evaluate
their accuracy with and without noise in input data and gauge their sensitivity
against algorithmic parameter changes. In this work, we achieve those tasks
through extensive experiments involving carefully designed synthetic and
real-life signals. Based on our experimental observations, we comment on the
pros and cons of the considered SD algorithms as well as highlighting the best
practices, in terms of parameter selection, for the their successful operation.
The SD algorithms for both single- and multi-channel (multivariate) data fall
within the scope of our work. For multivariate signals, we evaluate the
performance of the popular algorithms in terms of fulfilling the mode-alignment
property, especially in the presence of noise.Comment: Resubmission with changes in the reference lis