  Time-frequency analysis is an important and challenging task in many
applications. Fourier and wavelet analysis are two classic methods that have
achieved remarkable success in many fields. They also exhibit limitations when
applied to nonlinear and non-stationary signals. To address this challenge, a
series of nonlinear and adaptive methods, pioneered by the empirical mode
decomposition method have been proposed. Their aim is to decompose a
non-stationary signal into quasi-stationary components which reveal better
features in the time-frequency analysis. Recently, inspired by deep learning,
we proposed a novel method called residual recursive convolutional neural
network (RRCNN). Not only RRCNN can achieve more stable decomposition than
existing methods while batch processing large-scale signals with low
computational cost, but also deep learning provides a unique perspective for
non-stationary signal decomposition. In this study, we aim to further improve
RRCNN with the help of several nimble techniques from deep learning and
optimization to ameliorate the method and overcome some of the limitations of
this technique.
