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This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal's useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and local Rényi entropy estimation which are limited over short time blocks of the signal spectrogram. Local estimation of the signal features reduces the denoising problem to the stationary noise case. Results, presented for synthetic and real data, show consistently better performance gained by the proposed adaptive method compared to denoising driven by global criteria. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic method for extraction of useful information content from time-frequency distributions of nonstationary signals heavily corrupted by additive noise. The proposed method, which does not require prior knowledge of the signal, initially performs a one-dimensional clustering of the time-frequency distribution aimed at segmenting it into a fixed number of classes. This procedure points out basic structural differences of noise and signal components (i.e., useful information) in the time-frequency plane. In fact, noise presents a flat spectrum with large time-frequency supports, while signal components are narrow energy ridges. The time-frequency supports are estimated for each of the obtained classes and used as input for an algorithm aimed at discriminating “noise” affine classes, containing mainly non-zero coefficients located outside the components time-frequency supports, from “useful information” affine classes, containing mainly coefficients inside time-frequency supports of the signal components. Simulations show the superiority of the proposed algorithm, mainly in terms of error rate reduction of classified time-frequency coefficients, compared to hard amplitude thresholding methods, as well as to a recently proposed method for information content extraction based on adaptive thresholding, which is also outperformed in terms of computational complexity and execution time (by up to 43 and 28 times, respectively). [ABSTRACT FROM AUTHOR]

We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels. [ABSTRACT FROM AUTHOR]