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This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t–f) approach. The proposed algorithm exploits the direction of signal energy in the t–f domain to detect spikes in the presence of high-frequency nonstationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mimicking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector. [ABSTRACT FROM AUTHOR]

This study proposes a new time-frequency (TF) method for the recovery of missing samples from multicomponent signals. This is achieved by a combination of a sparsity-aware TF signal analysis method with TF filtering technique. A sparsity-aware TF method overcomes distortions caused by missing samples in the TF domain. This is followed by the use of TF filtering techniques for recovery of signals. All the extracted components are then combined to recover the complete signal. The proposed method outperforms other signal recovery methods such as gradient descent algorithm and matching pursuit. [ABSTRACT FROM AUTHOR]

Multi-component characteristics and missing data samples introduce artifacts and cross-terms in quadratic time–frequency distributions, thus affecting their readability. In this study, we propose a new time–frequency method that employs directional smoothing and compressive sensing to reduce cross-terms and mitigate artifacts associated with missing samples. The efficacy of the proposed time–frequency distribution for solving real-life problems is illustrated by employing it to estimate direction of arrival of sparsely sampled sources in under-determined scenario. Numerical results show that the proposed method is superior to other state-of-the-art methods both in terms of obtaining clear time–frequency representation and accurately estimating direction of arrival. [ABSTRACT FROM AUTHOR]