Abstract
The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages of the proposed technique is that it does not require any prior knowledge on the signal, its components, or noise, but rather the processing is performed on the noisy signal mixtures. Also, it is shown that the method is robust to the selection of time-frequency distributions (TFDs). It has been tested for different signal-to-noise-ratios (SNRs), both for synthetic and real-life data. When compared to fixed TFD thresholding, adaptive TFD thresholding based on RICI rule and the 1D entropy-based approach, the proposed adaptive method significantly increases classification accuracy (by up to 11.53%) and F1 score (by up to 7.91%). Hence, this adaptive, data-driven, entropy-based technique is an efficient tool for extracting useful information from noisy data in the time-frequency domain.
Highlights
Various real-life phenomena produce signals that contain information on the systems of their origin
When compared to fixed time-frequency distribution (TFD) thresholding, adaptive TFD thresholding based on relative intersection of confidence intervals (RICI) rule and the 1D entropy-based approach, the proposed adaptive method significantly increases classification accuracy and F1 score
We present a method for blind source separation based on the local 2D windowed Rényi entropy of the signals TFD
Summary
Various real-life phenomena produce signals that contain information on the systems of their origin. When analyzing underlying dynamics of these signals, most of them are non-stationary, meaning that their spectrum is time-varying and have dynamical spectral behavior (e.g., bio-medical signals, signals from radars, sonars, seismic activity, audio). Many real-life signals are multicomponent and may be decomposed to multiple amplitudes and/or frequency modulated components. The variables representing time and frequency are mutually exclusive. The time-frequency distribution (TFD) of the signal, when the signal has time-varying frequency content and dynamical spectral behavior, allows us to represent the signal jointly in time and Vrankovicet al. EURASIP Journal on Advances in Signal Processing (2020) 2020:18 frequency domain and to detect frequency components at each time instant [1]. TFDs are used in various fields, such as nautical studies [2], medicine [3, 4], electrical engineering [5, 6], and image processing [7, 8]
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