We propose a communication-efficient scheme for distributed Bayesian target tracking (distributed particle filtering) in possibly nonlinear and non-Gaussian state-space models. The scheme is a sparsity-promoting evolution of the likelihood consensus (LC) that uses the orthogonal matching pursuit (OMP), a B-spline dictionary, a distributed adaptive determination of the relevant state-space region, and an efficient binary representation of the LC expansion coefficients. Our simulation results show that a reduction of interagent communication by a factor of about 190 can be obtained without compromising the tracking performance.

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.

Some audio declipping methods produce waveforms that do not fully respect the actual process of clipping and allow a deviation on the reliable samples. This article reports what effect on perception it has if the output of such “inconsistent” methods is pushed towards “consistent” solutions by postprocessing. We first propose a simple sample replacement method, then we identify its main weaknesses and propose an improved variant. The experiments show that the vast majority of inconsistent declipping methods significantly benefit from the proposed approach in terms of objective perceptual metrics. In particular, we show that the SS PEW method based on social sparsity combined with the proposed method performs comparable to top methods from the consistent class, but at a computational cost of one order of magnitude lower.

The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experiments the methods are evaluated using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric.

In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator. This operator serves as the fundamental part of an iterative numerical solver. However, their mapping is improperly justified. The present article proves that their mapping is indeed a proximal operator, and also derives its proper counterpart. Furthermore, it is rationalized that Lieb and Stark’s operator can be understood as an approximation of the proper mapping. Surprisingly, in most cases, such an approximation (referred to as the approximal operator) is shown to provide even better numerical results in audio inpainting compared to its proper counterpart, while being computationally much more effective.

In this article, we deal with the problem of sparsity-based audio inpainting, i.e. filling in the missing segments of audio. A consequence of the approaches based on mathematical optimization is the insufficient amplitude of the signal in the filled gaps. Remaining in the framework based on sparsity and convex optimization, we propose improvements to audio inpainting, aiming at compensating for such an energy loss. The new ideas are based on different types of weighting, both in the coefficient and the time domains. We show that our propositions improve the inpainting performance in terms of both the SNR and ODG.

Methods based on sparse representation have found great use in the recovery of audio signals degraded by clipping. The state of the art in declipping within the sparsity-based approaches has been achieved by the SPADE algorithm by Kitić et. al. (LVA/ICA'15). Our recent study (LVA/ICA'18) has shown that although the original S-SPADE can be improved such that it converges faster than the A-SPADE, the restoration quality is significantly worse. In the present paper, we propose a new version of S-SPADE. Experiments show that the novel version of S-SPADE outperforms its old version in terms of restoration quality, and that it is comparable with the A-SPADE while being even slightly faster than A-SPADE.

An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the real-time phase gradient heap integration and the Gnann and Spiertz's real-time iterative spectrogram inversion with look-ahead. An extensive comparison with the state-of-the-art algorithms in a reproducible manner is presented.

The discrete wavelet transform module is a recent addition to the Large Time-Frequency Analysis Toolbox (LTFAT). It provides implementations of various generalizations of Mallat's well-known algorithm (iterated filterbank) such that completely general filterbank trees, dual-tree complex wavelet transforms, and wavelet packets can be computed. The resulting transforms can be equivalently represented as filterbanks and analyzed as filterbank frames using fast algorithms.

The IEEE 802.11 standard defines access categories (AC) and differentiated medium access control mechanisms for wireless local area networks. The preferential or deferral treatment of frames is achieved using configurable Arbitration Inter-Frame Spaces (AIFS) and customizable Contention Window (CW) sizes. In this paper, we address the problem of determining when a station, being a part of wireless communication, will access the medium. We present an algorithm calculating the probability of winning the contention by a given station, characterized by its AIFS and CW values. The probability of collision is calculated by similar means. The results were verified by simulations in Matlab and OPNET Modeler tools. We also introduce a web applet implementing and interactively demonstrating the results.