Abstract: 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.

Haya Alaskar, “Deep Learning-Based Model Architecture for Time-Frequency Images Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 9(12), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091268

@article{Alaskar2018,
title = {Deep Learning-Based Model Architecture for Time-Frequency Images Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091268},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091268},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {12},
author = {Haya Alaskar}
}