Distributed Signal Processing Algorithms for Multi-Task Wireless Acoustic Sensor Networks

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 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 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 signal processing task, such as noise reduction, echo cancellation, dereverberation, active noise control, or source localization. WASNs typically assume a setting in which all the nodes are of the same type and cooperate to solve a single network-wide signal processing task. Recently, however, WASNs have started to emerge in which the nodes cooperate with each other to solve multiple node-specific signal processing tasks, i.e., one (different) task for each node. These types of WASNs are referred to as multi-task WASNs. For instance, a WASN of this type can be established by connecting the daily-life heterogeneous devices such as smartphones, laptops, tablets, active noise control headphones, or hearing aids that wish to cooperate and share their microphone signals to enhance the performance of their own acoustic signal processing task. This thesis aims at developing novel distributed signal processing algorithms 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 signal processing algorithms developed in this thesis aim at letting each node of a multi-task WASN obtain the centralized solution of its corresponding node-specific signal processing task, although nodes cooperate with a significantly reduced-bandwidth signal transmission relying on compressive filter-and-sum operations.