Adaptive Time–Frequency Signal Decomposition Using Deep Learning for Intelligent Spectral Analytics

Abstract

Timefrequency (TF) analysis is an essential method to describe non-stationary signals in a
very diverse set of contemporary engineering fields, such as wireless communication system,
biomedical signal diagnostics, radar sensing, and power system monitoring. Existing TF
analysis methods including the Short-Time Fourier Transform (STFT), Wavelet Transform
(WT) and Empirical Mode Decomposition (EMD) use fixed bases of analysis or heuristic
decomposition algorithms, both of which can be associated with inherent limits, such as
mode mixing, suboptimal time-frequency resolution, noise sensitivity and poor response to
dynamically changing signal representations. In order to overcome the associated difficulties,
the current paper comes up with a new Adaptive Deep Learning-driven Timefrequency
Signal Decomposition (ADL-TFSD) architecture, fortifying both data-driven representation
learning with signal processing priors towards intelligent spectral analytics. The solution has
a hybrid convolutionaltransformer neural network that has the capability to learn directly
task-adaptive TF representations directly on raw time-domain signals, without using pre
defined kernels or windowing functions. Multi-scale convolutional networks are suitable
to capture local temporal-spectral patterns, whereas a self-attention mechanism shows a
long-range temporal patterns and cross-frequency interactions within the image, thus
producing a finer separation of the overlapping signal parts. Besides, learning strategy based
on reconstruction guided with sparsity and smoothness constraints guarantees physically
meaningful, noise-resistant and interpretable TF representations. Substantial experimental
studies of synthetic multi-component signals and on real-world data prove the claim that the
proposed ADL-TFSD system significantly outperforms traditional TF algorithms in spectral
resolution, signal reconstruction, ability to philtre noise and finally good performance in
downstream analytic activities including feature extraction and classification. The findings
affirm that adaptive TF decomposition based on deep learning offers a solution of significant
power and flexibility to the conventional approaches to analysis as a potential substitute
to conventional analytical measures in the next generation of intelligent signal analysis in
diverse and changing settings.