Time-frequency signal processing: Today and future

Most real-life signals exhibit non-stationary characteristics. Processing of such signals separately in the time-domain or in the frequency-domain does not provide sufficient information as their spectral properties change over time. Traditional methods such as the Fourier transform (FT) perform a transformation from time-domain to frequency-domain allowing a suitable spectral analysis but looses the spatial/temporal information of the signal components. Hence, it is not easy to observe a direct relationship between the time and frequency characteristics of the signal. This makes it difficult to extract useful information by using only time- or frequency-domain analysis for further processing purposes. To overcome this problem, joint time-frequency (TF) methods are developed and applied to the analysis and representation of non-stationary signals. In addition to revealing a time-dependent energy distribution information, TF methods have successfully been utilized in the estimation of some parameters related to the analyzed signals. In this paper, we briefly summarize the existing methods and present several state-of-the-art applications of TF methods in the classification of biomedical signals. We also point out some future perspectives for the processing of non-stationary signals in the joint TF domain. (C) 2021 Elsevier Inc. All rights reserved.