A review of automatic recognition technology for bird vocalizations in the deep learning era

Ecological Informatics, Год журнала: 2022, Номер 73, С. 101927 - 101927

Опубликована: Ноя. 25, 2022

Язык: Английский

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The machine learning–powered BirdNET App reduces barriers to global bird research by enabling citizen science participation

PLoS Biology, Год журнала: 2022, Номер 20(6), С. e3001670 - e3001670

Опубликована: Июнь 28, 2022

The BirdNET App, a free bird sound identification app for Android and iOS that includes over 3,000 species, reduces barriers to citizen science while generating tens of millions observations globally can be used replicate known patterns in avian ecology.

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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Global birdsong embeddings enable superior transfer learning for bioacoustic classification

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Дек. 18, 2023

Automated bioacoustic analysis aids understanding and protection of both marine terrestrial animals their habitats across extensive spatiotemporal scales, typically involves analyzing vast collections acoustic data. With the advent deep learning models, classification important signals from these datasets has markedly improved. These models power critical data analyses for research decision-making in biodiversity monitoring, animal behaviour studies, natural resource management. However, are often data-hungry require a significant amount labeled training to perform well. While sufficient is available certain taxonomic groups (e.g., common bird species), many classes (such as rare endangered species, non-bird taxa, call-type) lack enough train robust model scratch. This study investigates utility feature embeddings extracted audio identify other than ones were originally trained on. We evaluate on diverse datasets, including different calls dialect types, bat calls, mammals amphibians calls. The vocalization consistently allowed higher quality general datasets. results this indicate that high-quality large-scale classifiers can be harnessed few-shot transfer learning, enabling new limited quantity Our findings reveal potential efficient novel tasks, even scenarios where few samples.

Язык: Английский

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Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Окт. 17, 2023

Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, pace of remains contentious. Here, we use bioacoustics metabarcoding measure post-agriculture in a global hotspot Ecuador. We show that community composition, not species richness, vocalizing vertebrates identified by experts reflects restoration gradient. Two automated measures - an acoustic index model bird composition derived from independently developed Convolutional Neural Network correlated well with (adj-R² = 0.62 0.69, respectively). Importantly, both reflected non-vocalizing nocturnal insects via metabarcoding. such monitoring tools, based on new technologies, can effectively monitor success recovery, using robust reproducible data.

Язык: Английский

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The Internet of Animals: what it is, what it could be

Trends in Ecology & Evolution, Год журнала: 2023, Номер 38(9), С. 859 - 869

Опубликована: Май 30, 2023

One of the biggest trends in ecology over past decade has been creation standardized databases. Recently, this included live data, formal linkages between disparate databases, and automated analytics, a synergy that we recognize as Internet Animals (IoA). Early IoA systems relate animal locations to remote-sensing data predict species distributions detect disease outbreaks, use inform management endangered species. However, meeting future potential concept will require solving challenges taxonomy, security, sharing. By linking sets, integrating automating workflows, enable discoveries predictions relevant human societies conservation animals.

Язык: Английский

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Learning to detect an animal sound from five examples

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102258 - 102258

Опубликована: Авг. 10, 2023

Automatic detection and classification of animal sounds has many applications in biodiversity monitoring behavior. In the past twenty years, volume digitised wildlife sound available massively increased, automatic through deep learning now shows strong results. However, bioacoustics is not a single task but vast range small-scale tasks (such as individual ID, call type, emotional indication) with wide variety data characteristics, most bioacoustic do come strongly-labelled training data. The standard paradigm supervised learning, focussed on large-scale dataset and/or generic pre-trained algorithm, insufficient. this work we recast event within AI framework few-shot learning. We adapt to detection, such that system can be given annotated start/end times few 5 events, then detect events long-duration audio—even when category was known at time algorithm training. introduce collection open datasets designed strongly test system's ability perform detections, present results public contest address task. Our analysis prototypical networks are very common used strategy they well enhanced adaptations for general characteristics sounds. systems high resolution capabilities best challenge. demonstrate widely-varying durations an important factor performance, non-stationarity, i.e. gradual changes conditions throughout duration recording. For fine-grained recognition without massive data, our powerful new method, outperforming traditional signal-processing methods fully automated scenario.

Язык: Английский

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Feature embeddings from the BirdNET algorithm provide insights into avian ecology

Ecological Informatics, Год журнала: 2023, Номер 74, С. 101995 - 101995

Опубликована: Янв. 18, 2023

Язык: Английский

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Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Heliyon, Год журнала: 2023, Номер 9(10), С. e20275 - e20275

Опубликована: Сен. 22, 2023

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses the distribution of biotic and abiotic sounds at different frequencies attribute relationship said with ecosystem health metrics indicators (e.g., species richness, biodiversity, vectors structural change, gradients vegetation cover, connectivity, temporal spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use ARUs capacity to record hours audio for months time have created need automatic processing methods reduce consumption, correlate variables implicit in recordings, extract features, characterize sound related attributes. Consequently, traditional machine learning been commonly used process data characteristics soundscapes, mainly presence–absence species. In addition, it has employed call segmentation, identification, source clustering. However, some authors highlight importance new approaches unsupervised deep improve results diversify assessed this paper, we present systematic review field ecoacoustics processing. includes recent trends, as semi-supervised methods. Moreover, maintains format found reviewed papers. First, describe papers analyzed, configuration, study sites where datasets were collected. Then, provide an ecological justification relates monitoring features. Subsequently, explain followed assess various show trend towards label-free can large volumes gathered years. Finally, discuss adopt approach other biological dimensions landscapes.

Язык: Английский

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Hearing to the Unseen: AudioMoth and BirdNET as a Cheap and Easy Method for Monitoring Cryptic Bird Species

Sensors, Год журнала: 2023, Номер 23(16), С. 7176 - 7176

Опубликована: Авг. 15, 2023

The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount data collected using such technique. development species-specific recognizers (e.g., deep learning) may alleviate time required for but are often difficult create. Here, we evaluate effectiveness BirdNET, a new machine learning tool freely available automated recognition and processing, correctly identifying detecting two cryptic forest bird species. BirdNET precision was high both Coal Tit (Peripatus ater) Short-toed Treecreeper (Certhia brachydactyla), with mean values 92.6% 87.8%, respectively. Using default values, successfully detected in 90.5% 98.4% annotated recordings, We also tested impact variable confidence scores on performance estimated optimal score each Vocal activity patterns species, PAM reached their peak during first hours after sunrise. hope that our study encourage researchers managers utilize this user-friendly ready-to-use software, thus contributing advancements sensing environmental monitoring.

Язык: Английский

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