Optimization of sensor deployment for acoustic detection and localization in terrestrial environments

The rapid evolution in miniaturization, power efficiency and affordability of acoustic sensors, combined with new innovations in smart capability, are vastly expanding opportunities in ground-level monitoring for wildlife conservation at a regional scale using massive sensor grids. Optimal placement of environmental sensors and probabilistic localization of sources have previously been considered only in theory, and not tested for terrestrial acoustic sensors. Conservation applications conventionally model detection as a function of distance. We developed probabilistic algorithms for near-optimal placement of sensors, and for localization of the sound source as a function of spatial variation in sound pressure. We employed a principled-GIS tool for mapping soundscapes to test the methods on a tropical-forest case study using gunshot sensors. On hilly terrain, near-optimal placement halved the required number of sensors compared to a square grid. A test deployment of acoustic devices matched the predicted success in detecting gunshots, and traced them to their local area. The methods are applicable to a broad range of target sounds. They require only an empirical estimate of sound-detection probability in response to noise level, and a soundscape simulated from a topographic habitat map. These methods allow conservation biologists to plan cost-effective deployments for measuring target sounds, and to evaluate the impacts of sub-optimal sensor placements imposed by access or cost constraints, or multipurpose uses.</p

Tropical forest gunshot classification training audio dataset

DATA SOURCE LOCATION Data were collected in tropical forest sites in central Belize. Data were recorded in Tapir Mountain Nature Reserve (TMNR) and the adjoining Pook&rsquo;s Hill Reserve in Cayo District, Belize [17.150, -88.860] and Manatee Forest Reserve (MFR) and surrounding protected areas in Belize District, Belize [17.260, -88.490]. FOLDERS The folders contain audio files recorded between 2017 and 2021. The &lsquo;Training data&rsquo; folder and the &lsquo;Validation data&rsquo; folder contain two temporally distinct datasets, which can be used for model training and validation. The training folder consists of 80% of the total dataset, and the validation folder comprises the remaining 20%. Within each of these folders are two folders labelled &lsquo;Gunshot&rsquo; and &lsquo;Background&rsquo;. FILES The folders contain 749 gunshot files and over 35,000 background files. The files are in Waveform Audio File Format (wav), and are each 4.09 seconds long. The first 8 alphanumeric characters of the file name corresponds to the UNIX hexadecimal timestamp of the time of recording. Some files contain additional alphanumeric characters after these initial 8 characters, which were used as unique identifying numbers during processing and do not convey any additional information. </span

Automated detection of gunshots in tropical forests using convolutional neural networks

Unsustainable hunting is one of the leading drivers of global biodiversity loss, yet very few direct measures exist due to the difficulty in monitoring this cryptic activity. Where guns are commonly used for hunting, such as in the tropical forests of the Americas and Africa, acoustic detection can potentially provide a solution to this monitoring challenge. The emergence of low cost autonomous recording units (ARUs) brings into reach the ability to monitor hunting pressure over wide spatial and temporal scales. However, ARUs produce immense amounts of data, and long term and large-scale monitoring is not possible without efficient automated sound classification techniques. We tested the effectiveness of a sequential two-stage detection pipeline for detecting gunshots from acoustic data collected in the tropical forests of Belize. The pipeline involved an on-board detection algorithm which was developed and tested in a prior study, followed by a spectrogram based convolutional neural network (CNN), which was developed in this manuscript. As gunshots are rare events, we focussed on developing a classification pipeline that maximises recall at the cost of increased false positives, with the aim of using the classifier to assist human annotation of files. We trained the CNN on annotated data collected across two study sites in Belize, comprising 597 gunshots and 28,195 background sounds. Predictions from the annotated validation dataset comprising 150 gunshots and 7044 background sounds collected from the same sites yielded a recall of 0.95 and precision of 0.85. The combined recall of the two-step pipeline was estimated at 0.80. We subsequently applied the CNN to an un-annotated dataset of over 160,000 files collected in a spatially distinct study site to test for generalisability and precision under a more realistic monitoring scenario. Our model was able to generalise to this dataset, and classified gunshots with 0.57 precision and estimated 80% recall, producing a substantially more manageable dataset for human verification. Using a classifier-guided listening approach such as ours can make wide scale monitoring of threats such as hunting a feasible option for conservation management