  Time-frequency images (TFIs) provide a joint time-frequency representation of
a signal and have become an effective tool for analyzing, characterizing, and
processing non-stationary signals. Deep learning (DL) techniques have become
versatile for signal classification, enabling the automatic extraction of
relevant features from raw data. In this paper, we present two use cases on the
time-frequency transformation and deep learning techniques for signal
classification, where signals are first pre-processed and transformed into
TFIs, and their features are then extracted through deep learning neural
networks and classification algorithms. The specific methods and algorithms
used may vary depending on the particular application, therefore different
methods for creating TFIs; the Short-Time Fourier Transform (STFT),
Fourier-based Synchrosqueezing Transform (FSST), Wigner Ville distribution
(WVD), Smoothed Pseudo-Wigner distribution (SPWD), Choi-Williams distribution
(CWD), and Continuous Wavelet Transform (CWT) are investigated. The performance
of various deep learning, and convolutional neural network (CNN) models such as
ResNet-50, ShuffleNet, and Squeezenet are evaluated for their accuracy of
classification in different applications and the results are compared with the
results of the conventional machine learning and ensemble methods such as
Multilayer Perceptrons (MLP), Support Vector Machine (SVM), Random Forest (RF),
Decision Tree (DT), and XGboost. The results of this research demonstrate that
significant improvements in signal classification accuracy can be achieved by
leveraging the combined power of TFIs, and deep learning models. These advances
have found practical applications in a wide range of fields, including radar
signal classification, stability analysis of power systems, speech and music
recognition, and biomedical signal characterization.
