A Survey of Sound Source Localization Methods in Wireless Acoustic Sensor Networks

Abstract

Wireless acoustic sensor networks (WASNs) are formed by a distributed group of acoustic-sensing devices featuring audio playing and recording capabilities. Current mobile computing platforms offer great possibilities for the design of audio-related applications involving acoustic-sensing nodes. In this context, acoustic source localization is one of the application domains that have attracted the most attention of the research community along the last decades. In general terms, the localization of acoustic sources can be achieved by studying energy and temporal and/or directional features from the incoming sound at different microphones and using a suitable model that relates those features with the spatial location of the source (or sources) of interest. This paper reviews common approaches for source localization in WASNs that are focused on different types of acoustic features, namely, the energy of the incoming signals, their time of arrival (TOA) or time difference of arrival (TDOA), the direction of arrival (DOA), and the steered response power (SRP) resulting from combining multiple microphone signals. Additionally, we discuss methods not only aimed at localizing acoustic sources but also designed to locate the nodes themselves in the network. Finally, we discuss current challenges and frontiers in this field.

Evidence-Efficient Multihop Clustering Routing Scheme for Large-Scale Wireless Sensor Networks

“…Under the worst situation, if the path e is forced to join the connected subsets, the loop inevitably emerges. This result is in contradiction with the basic idea of call Algoithm 2 (6) end (7) Sink node broadcasts cluster head information and minimum aggregation tree information (8) for each node i received the cluster head information and minimum aggregation tree information (9) if (node i is the CH) (10)…”

“…In the initial stage of each cycle for the proposed EEMCR, the base station calculates the orientation-tended matrix of the nodes in terms of the Formula (6) and broadcasts it to all nodes. The nodes that successfully receive the orientation-tended matrix calculate their own responsibility and availability according to Formulas (8) and (9). At the same time, for any node i, the algorithm takes the node k that maximizes the sum of ( , )+ ( , ) as the CH in the current cycle of iteration.…”

“…The related technique-efficient issues, such as clustering routing, topology control, and multihop transmission, are widely used to improve energy efficiency for WSNs [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. On the other hand, it is very important to note that hybridization combination of various approaches may affect total performance.…”

Mobile Synchronization Recovery for Ultrasonic Indoor Positioning

“…In a first step, a moving MD estimates the TO using only TDOA information using a mathematically closed form, in this work derived from the intersection of hyperboloids, which however is notoriously highly sensitive to measurements' uncertainty [34][35][36]. The MD estimates TO from scratch, i.e., without prior information.…”

Effective Acoustic Energy Sensing Exploitation for Target Sources Localization in Urban Acoustic Scenes

“…The Maximum Likelihood (ML) Energy-based source localization methods, ML-Energy [5] and H-ML-Energy [4], are examined before and after the application of the proposed approach. This solution is also compared to a sensor selection method based on noise reduction [3], [7]. Experiments are conducted with four different values of SNR (signal-to-noise ratio) ranging from 0dB to 15dB.…”