Designing high-resolution time-frequency and time-scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance

Machine learning methodologies using TF/TS features can result in the design of systems that improve the classification of non-stationary signals. Using selected TF distributions (TFDs) and TS distributions (TSDs), the extraction of such TF/TS features is demonstrated on multi-channel recordings using channel fusion or feature fusion approaches. Extending the findings of previous studies, a TF/TS feature set is formed by including two complementary categories: signal related features and image features. The design of high-resolution TF/TS algorithms is then refined to account for issues of accuracy and robustness. Then, the desired TF/TS features are selected using different feature selection algorithms and compared with respect to the classification performance. Finally, other features from related methods are added, and comparisons performed. Improvements of up to 5% are obtained when using the chosen feature set after wrapper feature selection with channel feature fusion.