Wireless Acoustic Sensor Networks and Applications

Maximo Cobos, Fabio Antonacci, Athanasios Mouchtaris, Bowon Lee
2017 Wireless Communications and Mobile Computing  
Acoustic array processing is today an essential part of many applications involving the analysis of audio signals, such as hearing aids, hands-free devices, or immersive audio recording. While a number of acoustic sensing and processing systems have been proposed over the last decades, these have typically relied on high-throughput computing platforms and/or expensive microphone arrays. Although microphone arrays yield a higher performance than single-microphone systems, some limitations arise
more » ... rom the fact that the position of the microphones tend to be fixed, and all the signal processing tasks are performed on a centralized processor. The alternative is to use comparatively low-resource, distributed nodes with sensing devices and algorithms aimed at detecting, localizing, or characterizing acoustic events. The advantage of these systems is that the wireless, batterypowered nodes are less expensive and can be easily deployed in a wide range of environments. Moreover, as opposed to traditional microphone arrays that sample a sound field only locally, distributed acoustic sensing systems allow using many more sensors to cover a large area of interest. Signal processing and machine learning research for advanced acoustic systems of this type is giving birth to emerging technologies and services with a great exploitation potential. Current application domains such as smart cities and buildings, ambient assisted living, or habitat monitoring have already demonstrated the interest for acoustic-based solutions. Internet of Things (IoT) platforms and singleboard computers have substantially increased the capabilities of sensor networks aimed at acoustic signal processing, opening new possibilities, and challenges for making of sound a valuable source of information for the development of new services. Therefore, audio signal processing and machine learning for Wireless Acoustic Sensor Networks (WASNs) has attracted the interest of many authors. The article by M. Cobos et al., coauthored by the guest editors of this special issue, provides an extensive survey of the current state of the art of sound localization approaches in WASNs. The article assumes the case of a fusion center where localization takes place and considers both the case of single and multiple microphones at each WASN node. The most popular approaches for sound source localization are presented, including approaches based on signal energy, Time of Arrival (TOA), Time Difference of Arrival (TDOA), Direction of Arrival (DOA), and steered-response-power (SRP) methodologies. The problem of estimating the node locations (typically referenced as self-localization) is also considered. The article concludes by posing significant challenges in this area which are still open and call for further research efforts. One of the most common approaches for source localization in WASNs is based on DOAs and envisions the cooperation of multiple nodes, each estimating a DOA. A three-dimensional source location estimate typically requires each node to provide azimuth and elevation of the sources in the acoustic scene to the central node, with a negative impact on the hardware costs, as in each node microphones
doi:10.1155/2017/1085290 fatcat:c2ep4rq77jfslhq4nic7xnluxy