Advanced Spectral and Statistical Learning Methods for Intelligent Interpretation of Non-Stationary Signals

Abstract

The example of Non-stationary signals is common in signal processing because of application
in biomedical monitoring, speech communication, radar sensing, and industrial diagnostics
alike where the nature of signals changes dynamically with time. The conventional spectral
analysis methods are usually not capable of attaining sufficient time aspects and the spectral
strength in these circumstances, which confines them in reading intelligent signals. In a bid to
overcome such challenges, this paper introduces a developed spectral and statistical learning
framework to the issue of intelligent analysis of non-stationary signals. The proposed method
is a combination of adaptive spectral decomposition and statistical learning techniques to
identify discriminative time frequency representations that are realistic in time frequency
dynamics of signals. Enhanced adaptive deployments strategies are used to refine key
spectral components, and then it is followed by extraction of statistical descriptors, including
energy distribution, entropy, and higher-order moments. An intelligent learning module
then utilises these characteristics to create stable signal interpretation in diverse non noise
and non-stationary situations. The large-scale experimental assessments of synthetic and
real life non-stationary signals indicate that the proposed framework is always better than
traditional spectral and learning-based approaches in signal-to-noise ratio, peak signal-to
noise ratio, spectral entropy, and mean square error. Moreover, the findings support the
effectiveness and processing efficiency of the presented solution, which proves its use in
real-time signal analytics. The suggested framework offers a solution to the problem of
sophisticated spectral analysis and smart processing of the complicated non-stationary
signals in an efficient and scalable way.