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.

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