Localizing multiple audio sources in a wireless acoustic sensor network

Griffin, Anthony; Alexandridis, Anastasios; Pavlidi, Despoina; Mastorakis, Yiannis; Mouchtaris, Athanasios

doi:10.1016/j.sigpro.2014.08.013

Cited by 80 publications

(51 citation statements)

References 24 publications

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“…To attain the accuracy of nonlinear least squares estimators and improve their computational complexity, the gridbased (GB) method [70,71] is based on making the search space discrete by constructing a grid G of grid points over the localization area. Moreover, as the measurements are angles, the GB method proposes the use of the Angular Distance-taking values in the range of [0, ]-as a more proper measure of similarity than the absolute distance of (42).…”

Section: Single Source Localization Through Doasmentioning

confidence: 99%

“…Then, the best grid point in this new grid is found and the procedure is repeated until the desired accuracy is obtained, while keeping the complexity under control, as it does not require an exhaustive search over a large number of grid points. In [70] it is shown that the GB method is much more computationally efficient than the nonlinear least squares estimators and attains the same accuracy.…”

Section: Single Source Localization Through Doasmentioning

confidence: 99%

“…In that way, for all initial locations, the estimator is expected to converge to a location of a true source. However, as illustrated in [70], in the presence of missed detections and high noise the approach is not able to completely eliminate "ghost" sources. The multiple source grid-based method [70] estimates an initial location for each possible DOA combination from the sensors by solving (47).…”

Section: Multiple Source Localizationmentioning

confidence: 99%

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

Cobos

Antonacci

Alexandridis

et al. 2017

Wireless Communications and Mobile Computing

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105

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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.

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Section: Single Source Localization Through Doasmentioning

confidence: 99%

Section: Single Source Localization Through Doasmentioning

confidence: 99%

Section: Multiple Source Localizationmentioning

confidence: 99%

See 1 more Smart Citation

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

Cobos

Antonacci

Alexandridis

et al. 2017

Wireless Communications and Mobile Computing

Self Cite

105

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show abstract

“…The second way of estimating absolute location would be to combine DOA estimates from multiple recording nodes to triangulate the vocalizing bird in three-dimensional space (Wang et al 2005, Ali et al 2009, Griffin et al 2015. The principles of this approach are similar to those in radio telemetry studies, where an animal's location is estimated based on the intersection of bearings to the animal's radio transmitter measured from different locations (White and Garrott 1990).…”

Section: Doa Estimation For Monitoring Movementsmentioning

confidence: 99%

Direction-of-arrival estimation of animal vocalizations for monitoring animal behavior and improving estimates of abundance

Hedley¹,

Huang²,

Yao³

2017

ACE

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ABSTRACT. Autonomous recording units (ARUs) show promise for improving the spatial and temporal coverage of biodiversity monitoring programs, and for improving the resolution with which the behaviors of animals can be monitored on small spatial scales. Most ARUs, however, provide the user with little to no ability to determine the direction of an incoming sound, a shortcoming that limits the utility of ARU recordings for assessing the abundance of animals. We present a recording system constructed from two Wildlife Acoustics SM3 recording units that can estimate the direction-of-arrival (DOA) of an incoming signal with high accuracy. Field tests of this system revealed that 95% of sounds were estimated within 12° of the true DOA in the azimuth angle and 9° in the elevation angle, and that the system was largely robust to background noise and accurate to at least 30 m. We tested the ability of the system to discriminate up to four simulated birds singing simultaneously and show that the system generally performed well at this task, but, as expected, fainter and longer sounds were more likely to be overlapped and therefore undetected by the system. We propose that a microphone system that can estimate the DOA of sounds, such as the system presented here, may improve the ability of ARUs to assess abundance during biodiversity surveys by facilitating more accurate localization of sounds in three dimensions. Estimation de la direction d'arrivée des vocalisations animales pour le suivi comportemental d'animaux et l'amélioration des estimations d'abondanceRÉSUMÉ. Les instruments d'enregistrement autonomes (IEA) sont prometteurs pour améliorer la couverture spatiale et temporelle des programmes de suivi de la biodiversité et améliorer la résolution à laquelle le comportement des animaux peut être suivi sur de petites échelles spatiales. Toutefois, la plupart des IEA ne permettent pas (ou très peu) à l'utilisateur de déterminer la direction d'un son; cette lacune limite l'utilité des enregistrements issus d'IEA pour ce qui est de l'évaluation de l'abondance des animaux. Dans la présente étude, nous présentons un système d'enregistrement conçu à l'aide de deux unités d'enregistrement « Wildlife Acoustics SM3 » qui peuvent estimer la direction d'arrivée (DA) d'un son avec une grande précision. Des tests de ce système sur le terrain ont révélé que 95 % des sons ont été estimés à l'intérieur de 12° de la DA réelle dans l'angle d'azimut et de 9° dans l'angle d'élévation; le système était très fiable malgré les bruits de fond et précis au moins jusqu'à 30 m. Nous avons testé la capacité du système à discriminer jusqu'à quatre oiseaux chantant simultanément (que nous avons simulés) et avons montré que le système performe généralement bien, mais comme attendu, les sons plus faibles et plus longs étaient davantage susceptibles d'être superposés et donc non détectés par le système. Nous croyons qu'un système de microphone qui permet d'estimer la DA des sons, comme le système ici présenté, peut améliorer la capacité des IEA à évalu...

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“…Here a statistical framework is invoked to provide an efficient localization algorithm. Unsynchronized communication between wireless acoustic sensors is studied in [11] where bearing-only information is exchanged between the network nodes. In all these cases, the conversion from time delay estimates to range estimates (where required) is based on the assumption of a common and known speed of sound.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Source Localization under Variable Speed of Sound Conditions

Rabenstein

Annibale

2017

Wireless Communications and Mobile Computing

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The rich literature on acoustic source localization mostly relies on the assumption of a constant value for the speed of sound. This hypothesis allows establishing simple relations between range differences and time differences and leads to effective estimation algorithms. However, it must be challenged for certain applications of wireless acoustic sensor networks in multizone buildings and outdoor environments. This article revisits the source localization problem for the more general case of an unknown value for the speed of sound. It reviews the physical foundations for the dependence of the speed of sound on the air temperature and presents the essential approaches to acoustic source localization. On this basis, several methods for source localization under uncertain or variable speed of sound conditions from the literature are discussed. Applications from different fields are shown. They comprise the localization of sources, sensors, and reflecting surfaces, time-difference-of-arrival disambiguation, and the direct determination of the speed of sound or the air temperature from acoustic measurements.

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Localizing multiple audio sources in a wireless acoustic sensor network

Cited by 80 publications

References 24 publications

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

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

Direction-of-arrival estimation of animal vocalizations for monitoring animal behavior and improving estimates of abundance

Acoustic Source Localization under Variable Speed of Sound Conditions

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