No abstract

This paper explores three groups of time–frequency distributions: the Cohen’s, affine, and reassigned classes of time–frequency representations (TFRs). This study provides detailed insight into the theory behind the selected TFRs belonging to these classes. Extensive numerical simulations were performed with examples that illustrate the behavior of the analyzed TFR classes in the joint time–frequency domain. The methods were applied both on synthetic and real-life non-stationary signals. The obtained results were assessed with respect to time–frequency concentration (measured by the Rényi entropy), instantaneous frequency (IF) estimation accuracy, cross-term presence in the TFRs, and the computational cost of the TFRs. This study gives valuable insight into the advantages and limitations of the analyzed TFRs and assists in selecting the proper distribution when analyzing given non-stationary signals in the time–frequency domain.

No abstract

This paper presents a method for classifying noisy, non-stationary signals in the time-frequency domain using artificial intelligence. The preprocessed time-series signals are transformed into time-frequency representations (TFrs) from Cohen's class resulting in the TFr images, which are used as input to the machine learning algorithms. We have used three state-of-the-art deep-learning 2d convolutional neural network (Cnn) architectures (ResNet-101, Xception, and EfficientNet). The method was demonstrated on the challenging task of detecting gravitational-wave (gw) signals in intensive real-life, non-stationary, non-gaussian, and non-white noise. The results show excellent classification performance of the proposed approach in terms of classification accuracy, area under the receiver operating characteristic curve (roC auC), recall, precision, F1 score, and area under the precision-recall curve (PR AUC). The novel method outperforms the baseline machine learning model trained on the time-series data in terms of all considered metrics. The study indicates that the proposed technique can also be extended to various other applications dealing with non-stationary data in intensive noise.

No abstract