Soundscape mapping: understanding regional spatial and temporal patterns of soundscapes incorporating remotely-sensed predictors and wildfire disturbance

Environmental Research Ecology, Journal Year: 2024, Volume and Issue: 3(2), P. 025002 - 025002

Published: May 15, 2024

Abstract Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination biophony (animal sound) geophony (wind, rain, other phenomena) represent the soundscape and, in comparison anthropophony (technological human can highlight valuable landscapes both communities. However, recording these sounds requires intensive deployment devices storage interpretation large amounts data, resulting data gaps across landscape periods which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, indices bridge observations provide insight larger spatial extents during interest. Here, we seven acoustically-derived bird species richness heterogeneous composed densely urbanized, suburban, rural, protected, recently burned lands Sonoma County, California, U.S.A., explore spatiotemporal patterns measurements. Predictive models driven by land-use/land-cover, remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, phenology variables capture daily differences with varying performance (avg. R 2 = 0.38 ± 0.11) depending on metric period-of-day interpretable sound related activity, weather phenomena, activity. We also offer case study data-driven prediction soniferous activity before (1–2 years prior) after post) wildfires our area find may depict reorganization following wildfires. This is demonstrated an upward trend 1–2 post-wildfire, particularly more severely areas. Overall, evidence importance spaceborne-lidar-derived forest phenological time series characteristics modeling upscale site map biodiversity areas without prior collection. Resulting maps identify attention occur at edge disturbances.

Language: Английский

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Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 949, P. 174868 - 174868

Published: July 20, 2024

Passive Acoustic Monitoring (PAM), which involves using autonomous record units for studying wildlife behaviour and distribution, often requires handling big acoustic datasets collected over extended periods. While these data offer invaluable insights about wildlife, their analysis can present challenges in dealing with geophonic sources. A major issue the process of detection target sounds is represented by wind-induced noise. This lead to false positive detections, i.e., energy peaks due wind gusts misclassified as biological sounds, or negative, noise masks presence sounds. dominated makes vocal activity unreliable, thus compromising and, subsequently, interpretation results. Our work introduces a straightforward approach detecting recordings affected windy events pre-trained convolutional neural network. facilitates identifying wind-compromised data. We consider this dataset pre-processing crucial ensuring reliable use PAM implemented preprocessing leveraging YAMNet, deep learning model sound classification tasks. evaluated YAMNet as-is ability detect tested its performance Transfer Learning scenario our annotated from Stony Point Penguin Colony South Africa. achieved precision 0.71, recall 0.66, those metrics strongly improved after training on dataset, reaching 0.91, 0.92, corresponding relative increment >28 %. study demonstrates promising application bioacoustics ecoacoustics fields, addressing need wind-noise-free released an open-access code that, combined efficiency peak be used standard laptops broad user base.

Language: Английский

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Improving the quality of the acoustic environment in neonatal intensive care units: a review of scientific literature and technological solutions

Frontiers in Computer Science, Journal Year: 2023, Volume and Issue: 5

Published: Oct. 5, 2023

There is an increased awareness of how the quality acoustic environment impacts lives human beings. Several studies have shown that sound pollution has adverse effects on many populations, from infants to adults, in different environments and workplaces. Hospitals are susceptible require special attention since can aggravate patients' health issues negatively impact performance healthcare professionals. This paper focuses Neonatal Intensive Care Units (NICU) as especially sensitive case representing a hostile which professionals little unwanted sounds perceived soundscape. We performed semi-systematic review scientific literature assessment NICU 2001. A thematic analysis was identify emerging themes informed 27 technological solutions for indoor outdoor environments. Solutions were categorized by functions evaluation methods grouped according characteristics design components, i.e., acquisition, computation, communication strategies. Results highlight lack assess qualitative such forecast footprint sources Such urgently needed empower professionals, nurses, actively modify prevent negative critical care

Language: Английский

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Automated cataloging of oyster toadfish (Opsanus tau) boatwhistle calls using template matching and machine learning

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102268 - 102268

Published: Aug. 22, 2023

Oyster toadfish (Opsanus tau) represent an ecologically significant species found throughout estuaries along the eastern coast of United States. While these crevice-dwelling fish can be challenging to observe in their habitats, it is possible infer distribution and aspects behavior by recording sounds they produce. The task cataloging distinctive advertisement boatwhistle produced male attract females spring summer automated using a multi-step process. Candidate boatwhistles are first identified template matching suite synthetic spectrogram kernels formed mimic two lowest frequency harmonic tones within boatwhistle. calls based on correlation between low-frequency data. Next, frequency-reassigned images candidates input into pre-trained ResNet-50 convolutional neural network. Finally, activations from deep, fully connected layer this network extracted passed one-vs-all support-vector-machine classifier, which separates larger set candidate signals. This classifier model was trained evaluated labeled dataset over 20,000 signals generated diverse acoustic conditions Pamlico Sound, North Carolina, USA. accompanying software provides effective efficient tool monitor calls, may facilitate deeper understanding spatial distribution, behavioral patterns, ecological roles played oyster toadfish.

Language: Английский

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Leveraging time-based acoustic patterns for ecosystem analysis

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(32), P. 20513 - 20526

Published: Aug. 13, 2024

Abstract Passive acoustic monitoring (PAM) is an effective, non-intrusive method for studying ecosystems, but obtaining meaningful ecological information from its large number of audio files challenging. In this study, we take advantage the expected animal behavior at different times day (e.g., higher activity dawn) and develop a novel approach to use these time-based patterns. We organize PAM data into 24-hour temporal blocks formed with sound features pretrained VGGish network. These feed 1D convolutional neural network class activation mapping technique that gives interpretability outcomes. As result, diel-cycle offer more accurate robust hour-by-hour than using traditional indices as features, effectively recognizing key ecosystem

Language: Английский

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