Deep learning-based model architecture for time-frequency images analysis

Time-frequency analysis is an initial step in the design of invariant representations for any type of time series signals. Time-frequency analysis has been studied and developed widely for decades, but accurate analysis using deep learning neural networks has only been presented in the last few years. In this paper, a comprehensive survey of deep learning neural network architectures for time-frequency analysis is presented and compares the networks with previous approaches to time-frequency analysis based on feature extraction and other machine learning algorithms. The results highlight the improvements achieved by deep learning networks, critically review the application of deep learning for time-frequency analysis and provide a holistic overview of current works in the literature. Finally, this work facilitates discussions regarding research opportunities with deep learning algorithms in future researches.