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Time-frequency images (TFIs) provide a joint time-frequency representation ofa signal and have become an effective tool for analyzing, characterizing, andprocessing non-stationary signals. Deep learning (DL) techniques have becomeversatile for signal classification, enabling the automatic extraction ofrelevant features from raw data. In this paper, we present two use cases on thetime-frequency transformation and deep learning techniques for signalclassification, where signals are first pre-processed and transformed intoTFIs, and their features are then extracted through deep learning neuralnetworks and classification algorithms. The specific methods and algorithmsused may vary depending on the particular application, therefore differentmethods 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 performanceof various deep learning, and convolutional neural network (CNN) models such asResNet-50, ShuffleNet, and Squeezenet are evaluated for their accuracy ofclassification in different applications and the results are compared with theresults of the conventional machine learning and ensemble methods such asMultilayer Perceptrons (MLP), Support Vector Machine (SVM), Random Forest (RF),Decision Tree (DT), and XGboost. The results of this research demonstrate thatsignificant improvements in signal classification accuracy can be achieved byleveraging the combined power of TFIs, and deep learning models. These advanceshave found practical applications in a wide range of fields, including radarsignal classification, stability analysis of power systems, speech and musicrecognition, and biomedical signal characterization.

Use Cases for Time-Frequency Image Representations and Deep Learning Techniques for Improved Signal Classification