Signal Processing and Time-Frequency Analysis
A special issue of Signals (ISSN 2624-6120).
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7642
Special Issue Editor
Interests: nonlinear signal processing; time-frequency signal representation; sparsity techniques; system identification; multisensor fusion; biomedical signal processing
Special Issue Information
Dear colleagues,
Time-frequency analysis (TFA) is a set of signal processing methods, techniques, and algorithms based on two types of variables, i.e., time and frequency. It is an alternative to traditional approaches in which time or frequency is used independently.
TFA is an approach that works well with non-stationary signals. The Nonstationarity of the signal means that there is a time-dependency of the signal frequency spectrum. In time-frequency algorithms, the variables of time and frequency are not mutually exclusive but present together. It is an important feature of the TFA that helps analyze non-stationary signals.
One of the most frequently used methods of time-frequency analysis is a short-time Fourier transform. The idea behind this method is to apply the Fourier transform to a portion of the signal.
Over recent years, the researcher proposed many other TFA methods, i.e., wavelet transform, Gabor transform, Wigner-Ville distribution, and Hilbert-Huang transform to name a few.
TFA methods can be applied to solve classical signal processing problems as denoising or detrending, can be a part of the recognition system to generate features in time-frequency domain (machine condition monitoring, speech recognition, etc.), image enhancement (from radars and sonars, etc.) and signal detection and image segmentation.
Therefore, the following contributions regarding Signal Processing and Time-Frequency Analysis are welcome:
TFA-based signal denoising and detrending;
TFA-based image enhancement;
TFA-based machine condition monitoring;
TFA-based speech processing and recognition;
TFA-based biomedical signal processing and feature extraction;
TFA-based seismological signal processing;
TFA-based signal change detection;
TFA-based image processing and segmentation.
Dr. Krzysztof Brzostowski
Guest Editor
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