Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 14, 2024

Abstract The urgency for remote, reliable, and scalable biodiversity monitoring amidst mounting human pressures on climate ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), but there been no comprehensive overview of its coverage across realms. We present metadata from 358 datasets recorded since 1991 above land water constituting the first global synthesis sampling spatial, temporal, ecological scales. compiled summary statistics (sampling locations, deployment schedules, focal taxa, recording parameters) used eleven case studies to assess trends biological, anthropogenic, geophysical sounds. Terrestrial is spatially denser (42 sites/M·km 2 ) than aquatic (0.2 1.3 oceans freshwater) with only one subterranean dataset. Although diel lunar cycles are well-covered all realms, marine (65%) comprehensively sample seasons. Across biological sounds show contrasting activity, while declining distance equator anthropogenic activity. PAM can thus inform phenology, macroecology, conservation studies, representation be improved by widening terrestrial taxonomic breadth, expanding high seas, increasing spatio-temporal replication freshwater habitats. Overall, shows considerable promise support efforts.

Language: Английский

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Soundscape Characterization Using Autoencoders and Unsupervised Learning

Sensors, Journal Year: 2024, Volume and Issue: 24(8), P. 2597 - 2597

Published: April 18, 2024

Passive acoustic monitoring (PAM) through recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, diversity, community interactions, human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due the large volumes ARU data. In this work, we propose a non-supervised framework using autoencoders extract soundscape features. We applied dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based autoencoder features represents cluster information with prototype spectrograms centroid decoder part neural network. analysis provides valuable insights into distribution temporal patterns various sound compositions within study area. By utilizing autoencoders, identify significant characterized recurring intense types across multiple frequency ranges. comprehensive understanding area's allows us pinpoint crucial sources gain deeper its environment. results encourage further exploration unsupervised algorithms as promising alternative path for environmental changes.

Language: Английский

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Using automated passive acoustic monitoring to measure changes in bird and bat vocal activity around hedgerows of different ages

Biological Conservation, Journal Year: 2024, Volume and Issue: 296, P. 110722 - 110722

Published: July 19, 2024

Hedgerows are a semi-natural habitat that supports farmland biodiversity by providing food, shelter, and connectivity. Hedgerow planting goals have been set across many countries in Europe agri-environment schemes (AES) play key role reaching these targets. Passive acoustic monitoring using automated vocalisation identification (automated PAM), offers valuable opportunity to assess changes following AES implementation simple, community-level metrics, such as vocal activity of birds bats. To evaluate whether could be used indicate the effectiveness hedgerow future result-based or hybrid schemes, we surveyed twenty-four hedgerows England classified into chrono-sequence three age categories (New, Young, Old). We recorded 4466 h over course 30 days measured bird bat BirdNET for Kaleidoscope Vocal all birds, bats were modelled with predictors hedgerow, habitat, weather conditions occurring from maturity. show an increase Young Old compared New ones highlight elements surrounding landscape should considered when evaluating on communities. found high precision low species-level observations, argue may novel link payment component PAM results, incentivising effective management farmers landowners.

Language: Английский

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Exploring emergent soundscape profiles from crowdsourced audio data

Computers Environment and Urban Systems, Journal Year: 2024, Volume and Issue: 110, P. 102112 - 102112

Published: April 8, 2024

The key component of designing sustainable, enriching, and inclusive cities is public participation. soundscape an integral part immersive environment in cities, it should be considered as a resource that creates the acoustic image for urban environment. For planning professionals, this requires understanding constituents citizens' emergent experience. goal study to present systematic method analyzing crowdsensed data with unsupervised machine learning methods. This applies sound- scape experience collection low threshold aim analyze using methods give insights into perception quality. purpose, qualitative raw audio were collected from 111 participants Helsinki, Finland, then clustered further analyzed. We conclude analysis combined accessible, mobile crowdsensing enable results can applied track hidden experiential phenomena soundscape.

Language: Английский

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The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings

Frontiers in Ecology and Evolution, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 16, 2025

Passive acoustic monitoring has emerged as a useful technique for vocal species and contributing to biodiversity goals. However, finding target sounds without pre-existing recognisers still proves challenging. Here, we demonstrate how the embeddings from large model BirdNET can be used quickly easily find new sound classes outside original model’s training set. We outline general workflow, present three case studies covering range of ecological use cases that believe are common requirements in research management: invasive species, generating lists, detecting threatened species. In all cases, minimal amount class examples validation effort was required obtain results applicable desired application. The demonstrated success this method across different datasets taxonomic groups suggests wide applicability novel classes. anticipate will allow easy rapid detection which no current exist, both conservation

Language: Английский

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Letting ecosystems speak for themselves: An unsupervised methodology for mapping landscape acoustic heterogeneity

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106373 - 106373

Published: Feb. 1, 2025

Language: Английский

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Urban Blue-Green Spaces and tranquility: a comprehensive review of noise reduction and sensory perception integration

Journal of Asian Architecture and Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: March 19, 2025

Language: Английский

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An overview of the current state of knowledge and technology on techniques and procedures for signal processing, analysis, and accurate inference for transportation noise and vibration

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117314 - 117314

Published: March 1, 2025

Language: Английский

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Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 97 - 122

Published: Jan. 1, 2025

Language: Английский

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Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(4), P. e1013029 - e1013029

Published: April 28, 2025

Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison whole soundscape properties rapidly deliver broad from data, in contrast to detailed but time-consuming analysis individual bioacoustic events. However, a lack effective automated for data has impeded progress this field. Here, we show that machine learning (ML) be used unlock greater soundscapes. We showcase on diverse set tasks using three biogeographically independent datasets, each containing fish community (high or low), cover low) depth zone (shallow mesophotic) classes. supervised train models identify ecological classes sites report unsupervised clustering achieves whilst providing more understanding site groupings within data. also compare different approaches extracting feature embeddings recordings input ML algorithms: indices commonly by ecologists, pretrained convolutional neural network (P-CNN) trained 5.2 million hrs YouTube audio, CNN’s which were task (T-CNN). Although T-CNN performs marginally better across tasks, reveal P-CNN offers powerful tool generating marine as it requires orders magnitude less computational resources achieving near comparable performance T-CNN, with significant improvements indices. Our findings have implications ecology any habitat.

Language: Английский

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