Design of an unmanned aerial vehicle mounted system for quiet audio recording

Hioka, Yusuke; Kingan, Michael J.; Schmid, Gian; McKay, Ryan S.; Stol, Karl

doi:10.1016/j.apacoust.2019.06.001

Cited by 20 publications

(15 citation statements)

References 14 publications

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“…By combining these two promising technologies, otherwise labour and time‐intensive species monitoring is on the cusp of being revolutionized by remotely recorded sounds with drone‐mounted microphones. If the major drawback for using UAV in acoustic biomonitoring is the excessive UAV noise that can mask the targeted sound, new methods are already in progress, such as the development of signal processing algorithms that reduce noise in recording (Hioka, Kingan, Schmid, McKay, & Stol, ).…”

Section: Discussionmentioning

confidence: 99%

Listening and watching: Do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

et al. 2020

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With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide‐ranging, elusive and cryptic species or those that occur at low density. Here we compare two non‐invasive methods, passive acoustic monitoring (n = 12) and camera trapping (n = 53), to detect chimpanzees Pan troglodytes in a savanna‐woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than 10 days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method. Synthesis and applications. Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species‐loss.

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Section: Discussionmentioning

confidence: 99%

Listening and watching: Do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

et al. 2020

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“…However, as drone technology has matured, small quadcopters have become steadily quieter, and smaller, and further technical developments (e.g. Hioka et al 2019) could result in drones that are quiet enough to greatly reduce, if not eliminate, noise disturbance effects.…”

Section: Discussionmentioning

confidence: 99%

Airborne point counts: a method for estimating songbird abundance with drones

Somers

et al. 2020

Preprint

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Using drones to conduct airborne bioacoustic surveys is a potentially useful new way to estimate the abundance of vocal bird species. Here we show that by using two recording devices suspended from a quadcopter drone it is possible to estimate distances to birds with precision. In an experimental test, the mean error of our estimated distances to a broadcast song across 11 points between 0 and 100 m away was just 3.47 m. In field tests we compared 1-minute airborne counts with 5-minute terrestrial counts at 34 count locations. We found that the airborne counts yielded similar data to the terrestrial point counts for most of the 10 the songbirds included in our analysis, and that the effective detection radii were also similar. However, airborne counts significantly under-detected the Northern Cardinal (χ29 = 22.8, post-hoc test P = 0.007), which we attribute to a behavioral response to the drone. Airborne counts work best for species that vocalize close to the ground and have high frequency-range songs. Under those circumstances, airborne bioacoustics could have several advantages over ground-based surveys, including increased precision, increased repeatability, and easier access in difficult terrain. Further, we show that it is possible to do rapid surveys using airborne techniques, which could lead to the development of much more efficient survey protocols than are possible using traditional survey techniques.Lay SummaryWe show that it is possible to estimate the distance of singing birds from a drone, which then allows bird counts to be converted to true abundance or population densities.Using drones to count birds allows researchers to survey areas that may be difficult or dangerous to access on foot.Airborne counts are potentially a highly efficient and highly repeatable way to estimate populations of vocal bird species.

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“…Unsupervised approaches reduce the ego-noise using only the microphone array through beamforming [34]- [39], timefrequency spatial filtering [16], [17], or blind source separation [8], [16]. Delay-and-sum fixed beamforming has limited performance in improving the SNR [34], [35].…”

Section: A Ego-noise Reductionmentioning

confidence: 99%

“…Delay-and-sum fixed beamforming has limited performance in improving the SNR [34], [35]. Adaptive beamforming performs better, but requires the knowledge of the correlation matrix of the ego-noise, which is difficult to estimate when the noise is nonstationary [36]- [39]. Timefrequency spatial filtering (TFS) performs ego-noise reduction by exploiting the time-frequency sparsity of audio signals to estimate the DOA of the sound at each time-frequency bin and then formulate a spatial filter based on these instantaneous DOA estimations [16].…”

Section: A Ego-noise Reductionmentioning

confidence: 99%

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A Blind Source Separation Framework for Ego-Noise Reduction on Multi-Rotor Drones

Wang

Cavallaro

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Acoustic sensing from a multi-rotor drone is heavily degraded by the strong ego-noise produced by the rotating motors and propellers. To address this problem, we propose a blind source separation (BSS) framework that extracts a target sound from noisy multi-channel signals captured by a microphone array mounted on a drone. The proposed method addresses the challenging problem of permutation alignment, in extremely low signal-to-noise-ratio scenarios (e.g. SNR <-15 dB), by performing clustering on the time activities of the separated signals across frequencies. Since initialization plays an important role to the success of clustering, we propose a pre-processing algorithm which uses time-frequency spatial filtering (TFS) to generate a reference to pre-align the permutation. The pre-alignment not only improves the performance of clustering and permutation alignment, but also solves the target-channel selection problem for BSS. The proposed method integrates the advantages of both TFS and BSS. Experimental results with real-recorded data show that the proposed method is capable of processing the audio stream continuously in a blockwise manner and also remarkably outperforms the state of the art.

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Design of an unmanned aerial vehicle mounted system for quiet audio recording

Cited by 20 publications

References 14 publications

Listening and watching: Do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

Listening and watching: Do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

Airborne point counts: a method for estimating songbird abundance with drones

A Blind Source Separation Framework for Ego-Noise Reduction on Multi-Rotor Drones

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