Perspectives in machine learning for wildlife conservation

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Фев. 9, 2022

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders bio-logging devices. These new technologies the data they generate hold great potential for large-scale environmental monitoring understanding, but are limited by current processing approaches which inefficient how ingest, digest, distill into relevant information. We argue that machine learning, especially deep learning approaches, can meet this analytic challenge enhance our capacity, conservation of wildlife species. Incorporating ecological workflows could improve inputs population behavior models eventually lead integrated hybrid modeling tools, with acting constraints latter providing data-supported insights. In essence, combining domain knowledge, ecologists capitalize on abundance generated modern sensor order reliably estimate abundances, study mitigate human/wildlife conflicts. To succeed, approach will require close collaboration cross-disciplinary education between computer science communities ensure quality train a generation scientists conservation.

Язык: Английский

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Computational bioacoustics with deep learning: a review and roadmap

PeerJ, Год журнала: 2022, Номер 10, С. e13152 - e13152

Опубликована: Март 21, 2022

Animal vocalisations and natural soundscapes are fascinating objects of study, contain valuable evidence about animal behaviours, populations ecosystems. They studied in bioacoustics ecoacoustics, with signal processing analysis an important component. Computational has accelerated recent decades due to the growth affordable digital sound recording devices, huge progress informatics such as big data, machine learning. Methods inherited from wider field deep learning, including speech image processing. However, tasks, demands data characteristics often different those addressed or music analysis. There remain unsolved problems, tasks for which is surely present many acoustic signals, but not yet realised. In this paper I perform a review state art learning computational bioacoustics, aiming clarify key concepts identify analyse knowledge gaps. Based on this, offer subjective principled roadmap learning: topics that community should aim address, order make most future developments AI informatics, use audio answering zoological ecological questions.

Язык: Английский

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Deep learning as a tool for ecology and evolution

Methods in Ecology and Evolution, Год журнала: 2022, Номер 13(8), С. 1640 - 1660

Опубликована: Май 30, 2022

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing autonomous driving. It also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing population genetics phylogenetics, among other applications. relies on artificial neural networks predictive modelling excels at recognizing complex patterns. In this review we synthesize 818 studies using deep the context of ecology evolution to give a discipline‐wide perspective necessary promote rethinking inference approaches field. We provide an introduction machine contrast with mechanistic inference, followed by gentle primer learning. applications discuss its limitations efforts overcome them. practical biologists interested their toolkit identify possible future find that being rapidly adopted evolution, 589 (64%) published since beginning 2019. Most use convolutional (496 studies) supervised identification but tasks molecular data, sounds, data or video as input. More sophisticated uses biology are appear. Operating within paradigm, can be viewed alternative modelling. desirable properties good performance scaling increasing complexity, while posing unique challenges such sensitivity bias input data. expect rapid adoption will continue, especially automation biodiversity monitoring discovery from genetic Increased unsupervised visualization clusters gaps, simplification multi‐step analysis pipelines, integration into graduate postgraduate training all likely near future.

Язык: Английский

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Passive acoustic monitoring provides a fresh perspective on fundamental ecological questions

Functional Ecology, Год журнала: 2023, Номер 37(4), С. 959 - 975

Опубликована: Янв. 20, 2023

Abstract Passive acoustic monitoring (PAM) has emerged as a transformative tool for applied ecology, conservation and biodiversity monitoring, but its potential contribution to fundamental ecology is less often discussed, PAM studies tend be descriptive, rather than mechanistic. Here, we chart the most promising directions ecologists wishing use suite of currently available methods address long‐standing questions in explore new avenues research. In both terrestrial aquatic habitats, provides an opportunity ask across multiple spatial scales at fine temporal resolution, capture phenomena or species that are difficult observe. combination with traditional approaches data collection, could release from myriad limitations have, times, precluded mechanistic understanding. We discuss several case demonstrate estimation, population trend analysis, assessing climate change impacts on phenology distribution, understanding disturbance recovery dynamics. also highlight what horizon PAM, terms near‐future technological methodological developments have provide advances coming years. Overall, illustrate how can harness power ecological era no longer characterised by limitation. Read free Plain Language Summary this article Journal blog.

Язык: Английский

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BirdNET: applications, performance, pitfalls and future opportunities

Ibis, Год журнала: 2023, Номер 165(3), С. 1068 - 1075

Опубликована: Фев. 27, 2023

Automated recognition software is paramount for effective passive acoustic monitoring. BirdNET a free and recently developed bird sound recognizer. I performed literature review to evaluate the current applications performance of BirdNET, which growing in popularity but has been subject few assessments, provide recommendations future studies using BirdNET. Prior research employed wide range purposes have linked detections ecological processes or real‐world monitoring schemes. Among evaluated studies, average precision (% correctly identified) usually ranged around 72–85%, recall rate target species vocalizations detected) 33–84%. Some did not assess performance, hampers interpretation results may poorly informed decisions. Recommendations on how efficiency are provided. The impact confidence score threshold, user‐selected parameter as minimum reported, output although variable among consistent. use high thresholds increases percentage classified lowers proportion calls detected. selection an optimal depend priorities user goals. great tool automated it should be used with caution due inherent challenges identification. continued refinement suggests further improvements coming years.

Язык: Английский

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The Internet of Sounds: Convergent Trends, Insights, and Future Directions

IEEE Internet of Things Journal, Год журнала: 2023, Номер 10(13), С. 11264 - 11292

Опубликована: Март 7, 2023

Current sound-based practices and systems developed in both academia industry point to convergent research trends that bring together the field of Sound Music Computing with Internet Things. This paper proposes a vision for emerging Sounds (IoS), which stems from such disciplines. The IoS relates network Things, i.e., devices capable sensing, acquiring, processing, actuating, exchanging data serving purpose communicating sound-related information. In paradigm, merges under unique umbrella fields Musical Things Audio heterogeneous dedicated musical non-musical tasks can interact cooperate one another other things connected facilitate services applications are globally available users. We survey state art this space, discuss technological non-technological challenges ahead us propose comprehensive agenda field.

Язык: Английский

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Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe

IEEE Transactions on Pattern Analysis and Machine Intelligence, Год журнала: 2023, Номер 46(4), С. 2151 - 2170

Опубликована: Ноя. 17, 2023

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry academia. Conventional approaches most autonomous driving algorithms perform detection, segmentation, tracking, etc., a front or perspective view. As sensor configurations get more complex, integrating multi-source information different sensors representing features unified view come of vital importance. BEV inherits several advantages, as surrounding scenes intuitive fusion-friendly; objects desirable subsequent modules planning and/or control. The core problems lie (a) how to reconstruct the lost 3D via transformation BEV; (b) acquire ground truth annotations grid; (c) formulate pipeline incorporate sources views; (d) adapt generalize vary across scenarios. In this survey, we review recent works on provide an in-depth analysis solutions. Moreover, systematic designs approach are depicted well. Furthermore, introduce full suite practical guidebook improve performance tasks, including camera, LiDAR fusion inputs. At last, point out future research directions area. We hope report will shed some light community encourage effort perception. keep active repository collect work toolbox bag tricks at https://github.com/OpenDriveLab/Birds-eye-view-Perception .

Язык: Английский

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Limits to the accurate and generalizable use of soundscapes to monitor biodiversity

Nature Ecology & Evolution, Год журнала: 2023, Номер 7(9), С. 1373 - 1378

Опубликована: Июль 31, 2023

Abstract Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts investigate whether soundscapes could be used monitor diverse ecosystems. found that neither univariate indices nor machine learning models were predictive of species richness datasets but soundscape change was consistently indicative community change. Our findings indicate there are no common features biodiverse and should cautiously in conjunction more reliable in-person ecological surveys.

Язык: Английский

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Guidelines for appropriate use of BirdNET scores and other detector outputs

Journal of Ornithology, Год журнала: 2024, Номер 165(3), С. 777 - 782

Опубликована: Фев. 14, 2024

Язык: Английский

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Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

Ecological Indicators, Год журнала: 2024, Номер 164, С. 112146 - 112146

Опубликована: Май 20, 2024

Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable data in ecological research projects. Increased ease collecting these means that, currently, the main bottleneck ecoacoustic projects is often time required for manual analysis passively collected recordings. In this study we evaluate potential current limitations BirdNET-Analyzer v2.4, most advanced generic deep learning algorithm bird recognition to date, tool assess community composition through automated large-scale data. To end, 3 datasets comprising total 629 environmental soundscapes 194 different sites spread across 19° latitude span Europe. We analyze using both BirdNET listening by local expert birders, then compare results obtained two methods performance at level each single vocalization entire recording sequences (1, 5 or 10 min). Since provides confidence score identification, minimum thresholds can be used filter out identifications with low scores, thus retaining only ones. The volume did not allow us estimate species-specific taxa, so instead evaluated global selected optimized when consistently applied all species. Our analyses reveal that if sufficiently high threshold used. However, inevitable trade-off between precision recall does obtain satisfactory metrics same time. found F1-scores remain moderate (<0.5) studied, extended duration seem currently necessary provide minimally comprehensive picture target community. estimate, however, usage species- context-specific would substantially improve benchmarks study. conclude judicious use AI-based provided represent powerful method assist assessment data, especially duration.

Язык: Английский

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