Author:

Keywords:

SISTA

Abstract:

Recent technological advances in analogue and digital electronics as well as in hardware miniaturization have taken wireless sensing devices to another level by introducing low-power communication protocols, improved digital signal processing capabilities and compact sensors. When these devices perform a certain pre-defined signal processing (SP) task such as the estimation or detection of phenomena of interest, a cooperative scheme through wireless connections can significantly enhance the overall performance, especially in adverse conditions. The resulting network consisting of such connected devices (or nodes) is referred to as a wireless sensor network (WSN). In short, the advantage of WSNs compared to conventional fixed sensor arrays is related to the fact that they provide access to more physically-distributed sensors, yielding a more informative spatial sampling of the phenomena of interest and therefore leading to a superior performance of the SP task. In this thesis, we focus specifically on estimation tasks for signals, subspaces or parameters within such WSNs. In acoustical applications (e.g., speech enhancement) a variant of WSNs, called wireless acoustic sensor networks (WASNs) can be employed in which the sensing unit at each node consists of a single microphone or a microphone array. The nodes of such a WASN can then cooperate to perform a multi-channel acoustic SP task, such as multi-channel noise reduction, echo cancellation, dereverberation, active noise control (ANC), or source localization. In general, WASNs deal with the acquisition and estimation of audio content for which data has to be sampled, processed and transmitted at a higher rate than in traditional low-power and low-rate WSNs. Hence, nodes of a WASN indeed demand greater processing power, communication resources and therefore they exhibit higher power consumption as compared to most other types of WSNs. Therefore, it is critical to design highly efficient SP algorithms under which nodes of a WASN can cooperate and enhance their signal, subspace, or parameter estimation performance, subject to constraints in bandwidth, computational complexity, or energy consumption. WASNs typically assume a setting in which all the nodes are of the same type and cooperate to solve a single network-wide SP task. Recently, however, WASNs have started to emerge in which the nodes cooperate with each other to solve multiple node-specific SP tasks, i.e., one (different) task for each node. These types of WASNs are referred to as multi-task WASNs in which each node is interested in estimating a different set of signals or parameters as observed by its own reference sensors, leading to different node-specific SP tasks which are somehow related since the observed signals are often highly correlated across the sensors of different nodes. This thesis aims at developing novel distributed SP algorithms for signal, parameter and subspace estimation in such multi-task WASNs. Distributed processing provides an attractive alternative to centralized processing, since for the latter case all the uncompressed sensor signals of the entire WASN have to be aggregated and processed in one place (e.g., in a fusion centre), which demands a large communication bandwidth and therefore consumes a great deal of energy. In general, the distributed SP algorithms developed in this thesis aim at letting each node of a multi-task WASN obtain the centralized solution of its corresponding node-specific SP task, although nodes cooperate with a significantly reduced-bandwidth signal transmission relying on compressive filter-and-sum operations. The first part of the thesis focuses on designing distributed algorithms for multi-task WASNs where the node-specific SP tasks all rely on the same basic SP technique, which can include signal enhancement, beamforming, spectrum estimation, subspace estimation, or DOA estimation. Such multi-task WASNs are classified as homogeneous multi-task WASNs, since all these nodes locally apply the same basic SP technique to their sensor signal observations (as part of their pre-defined routine operations). For instance, a homogeneous multi-task WASN can be established inside an auditorium where multiple hearing aids wish to cooperate with each other through wireless links. In this scenario, the hearing aids locally apply the same basic noise reduction technique to solve their node-specific noise reduction tasks. The second part of the thesis develops distributed algorithms for multi-task WASNs where the node-specific SP tasks rely on different basic SP techniques. Such multi-task WASNs are classified as heterogeneous multi-task WASNs. The resulting distributed SP algorithms of this part attempt to provide a framework under which daily-life heterogeneous devices running particular SP techniques also become capable to readily exchange signals and enhance their estimation performance, without relying on a rigid set of pre-defined routine operations and even without having any prior knowledge about the SP techniques other nodes use to solve their SP tasks. For instance, a heterogeneous multi-task WASN in this category can be established in an environment where several multimedia devices such as smartphones, laptops, tablets, ANC headphones, or hearing aids cooperate and share fused microphone signals to enhance their own estimation performance using different node-specific SP techniques such as multi-channel Wiener filtering, minimum variance beamforming, or subspace-based DOA estimation. The third part of the thesis, provides a real-time experimental validation for the developed distributed algorithm in a fully adaptive and realistic speech enhancement scenario. This scenario is created using an acoustic sensor network with three collaborating microphone arrays. The output signals of the distributed algorithm are assessed by means of instrumental measures for both speech intelligibility and speech quality. Finally, the last chapter provides the conclusions, summarizes the contributions of this thesis and further discusses possible future research directions.