  The decomposition of non-stationary signals is an important and challenging
task in the field of signal time-frequency analysis. In the recent two decades,
many signal decomposition methods led by the empirical mode decomposition,
which was pioneered by Huang et al. in 1998, have been proposed by different
research groups. However, they still have some limitations. For example, they
are generally prone to boundary and mode mixing effects and are not very robust
to noise. Inspired by the successful applications of deep learning in fields
like image processing and natural language processing, and given the lack in
the literature of works in which deep learning techniques are used directly to
decompose non-stationary signals into simple oscillatory components, we use the
convolutional neural network, residual structure and nonlinear activation
function to compute in an innovative way the local average of the signal, and
study a new non-stationary signal decomposition method under the framework of
deep learning. We discuss the training process of the proposed model and study
the convergence analysis of the learning algorithm. In the experiments, we
evaluate the performance of the proposed model from two points of view: the
calculation of the local average and the signal decomposition. Furthermore, we
study the mode mixing, noise interference, and orthogonality properties of the
decomposed components produced by the proposed method. All results show that
the proposed model allows for better handling boundary effect, mode mixing
effect, robustness, and the orthogonality of the decomposed components than
existing methods.
