Fast 3D AML-Based Bird Song Estimation

Zhang, Jiawei; Kossan, George; Hedley, Richard; Hudson, Robert S.; Taylor, Charles; Yao, Kung; Bao, Ming

doi:10.1142/s2301385014400044

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…VoxNet included many of the desirable features of wildlife recording arrays (discussed next), such as robustness and self‐synchronizing capabilities. This platform was used in 12 acoustic localization papers (Ali et al, 2009; Ali et al, 2007; Cai, Collier, Girod, Hudson, et al, 2013; Cai, Collier, Girod, Lee, et al, 2013; Collier, Blumstein, et al, 2010; Collier, Kirschel, & Taylor, 2010; Harlow, Collier, Burkholder, & Taylor, 2013; Trifa, Girod, Collier, Blumstein, & Taylor, 2007; Vallejo & Taylor, 2009; Yu et al, 2016; Zhang et al, 2014). However, these arrays were not widely used outside of the research group that developed them, who noted their expensiveness and difficulty to maintain (Taylor, Huang, & Yao, 2016).…”

Section: Resultsmentioning

confidence: 99%

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Rhinehart

Chronister

Devlin

et al. 2020

Ecology and Evolution

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Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data.

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Section: Resultsmentioning

confidence: 99%

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Rhinehart

Chronister

Devlin

et al. 2020

Ecology and Evolution

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“…First, some algorithms, including the MUSIC algorithm employed here (Schmidt 1986), have been shown elsewhere to successfully discriminate the directions of two or more overlapping signals (Zhang et al 2014). Once the directions of sources have been identified, beamforming can be used to amplify a signal from a particular direction while filtering out sounds from other directions, as a means of enhancing the sound of interest (Zhang et al 2014). An alternative approach, feasible when using manual segmentation for the detection of sounds, is to ignore signals that are obviously overlapped and only localize sounds that occur in isolation.…”

Section: Overlapping Signals and Acoustic Maskingmentioning

confidence: 99%

“…Alternatively, if only azimuth angles are of interest, computational costs could be reduced by searching a coarser grid in the elevation angle, while maintaining high resolution in the azimuth angle. Other algorithms have also been shown to reduce computation costs significantly (Zhang et al 2014), and may be incorporated into future versions of the SDEer software. Figure 4h (a), 4i (b), and 4j (c).…”

Section: Computational Costsmentioning

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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“…For example, Fig. 1 shows the spectrograms of two types of broadband sources: engine sound of a Porsche vehicle and bird chirping [6]. The frequency domain sparse structure [7] illustrated in these figures may be leveraged to realize efficient CS.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

Zhang

Bao

et al. 2016

IEEE Signal Process. Lett.

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A One-Bit Joint Sparse Representation Direction of Arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization allow significant reduction of data to be transmitted to the fusion center without additional energy consumption requirement in the source coding/compression operation. Compared with existing compressive sensing based DOA estimation methods, the superiority of the proposed approach in providing data volume reduction and performance improvement is verified by both simulations and field experiments using a prototype wireless sensor array network platform.

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Fast 3D AML-Based Bird Song Estimation

Cited by 8 publications

References 8 publications

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

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

DOA Estimation From One-Bit Compressed Array Data via Joint Sparse Representation

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