Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model

Ecological Informatics, Год журнала: 2022, Номер 69, С. 101663 - 101663

Опубликована: Май 6, 2022

Язык: Английский

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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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Unsupervised classification to improve the quality of a bird song recording dataset

Ecological Informatics, Год журнала: 2022, Номер 74, С. 101952 - 101952

Опубликована: Дек. 12, 2022

Язык: Английский

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Automatic detection for bioacoustic research: a practical guide from and for biologists and computer scientists

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

ABSTRACT Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, corresponding increase volume data generated. However, sets are often becoming so sizable that analysing them manually is increasingly burdensome unrealistic. Fortunately, we also computing power capability machine learning algorithms, which offer possibility performing some analysis required PAM automatically. Nonetheless, field automatic detection events still its infancy biology ecology. In this review, examine trends bioacoustic their implications burgeoning amount needs to be analysed. We explore different methods other tools scanning, analysing, extracting automatically from large volumes recordings. then provide step‐by‐step practical guide using bioacoustics. One biggest challenges greater bioacoustics there gulf expertise between sciences computer science. Therefore, review first presents an overview requirements bioacoustics, intended familiarise those science background with community, followed by introduction key elements artificial intelligence biologist understand incorporate into research. building pipeline data, conclude discussion possible future directions field.

Язык: Английский

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DBS-NET: A Dual-Branch Network Integrating Supervised and Contrastive Self-Supervised Learning for Birdsong Classification

Applied Sciences, Год журнала: 2025, Номер 15(10), С. 5418 - 5418

Опубликована: Май 12, 2025

Birdsong classification plays a crucial role in monitoring species distribution, population structure, and environmental changes. Existing methods typically use supervised learning to extract specific features for classification, but this may limit the generalization ability of model lead errors. Unsupervised feature extraction are an emerging approach that offers enhanced adaptability, particularly handling unlabeled diverse birdsong data. However, their drawback bring additional time cost downstream tasks, which impact overall efficiency. To address these challenges, we propose DBS-NET, Dual-Branch Network Model classification. DBS-NET consists two branches: branch (Res-iDAFF) unsupervised (based on contrastive approach). We introduce iterative dual-attention fusion (iDAFF) module backbone enhance contextual extraction, linear residual classifier is exploited further improve accuracy. Additionally, class imbalance dataset, weighted loss function introduced adjust cross-entropy with optimized weights. training efficiency, networks both branches share portion weights, reducing computational overhead. In experiments self-built 30-class dataset Birdsdata proposed method achieved accuracies 97.54% 97.09%, respectively, outperforming other methods.

Язык: Английский

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Classification of birdsong spectrograms based on DR-ACGAN and dynamic convolution

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102250 - 102250

Опубликована: Авг. 7, 2023

Birdsongs are highly valuable for bird studies as they provide insights into various aspects such species distribution, population structures, and habitat. Recognizing birdsongs plays a crucial role in conservation efforts. However, manually collecting large number of from the natural environment is expensive time-consuming. Moreover, using limited birdsong data often results low classification accuracy models. To better identification birdsongs, we utilize wavelet transform(WT) to convert them spectrograms, which contain abundant energy frequency information. Effectively extracting these features vital improve model. address this problem, proposed an improved ACGAN model based on residual structure attention mechanism named DR-ACGAN, achieved stable training high-quality generated spectrograms. The dynamic convolution kernel then fused with MobileNetV2, ResNet18, VGG16 models trained different datasets, used ways mixing original experimental show that after augmentation improves by 6.66%, 4.35%, 2.29% compared dataset three base classifiers. After adding convolutional structure, further 1.68%, 0.67%, 0.38% average achieves highest 97.60%.

Язык: Английский

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One-step progressive representation transfer learning for bird sound classification

Applied Acoustics, Год журнала: 2023, Номер 212, С. 109614 - 109614

Опубликована: Сен. 1, 2023

Язык: Английский

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Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests

F1000Research, Год журнала: 2024, Номер 12, С. 1299 - 1299

Опубликована: Янв. 23, 2024

Background From passive acoustic monitoring (PAM) recordings, the vocal activity rate (VAR), vocalizations per unit of time, can be calculated and is essential for assessing bird population abundance. However, VAR subject to influences from a range factors, including species environmental conditions. Identifying optimal sampling design obtain representative data estimation crucial research objectives. PAM commonly uses temporal strategies decrease volume recordings resources needed audio management. Yet, comprehensive impact this approach on remains insufficiently explored. Methods In study, we used extracted 12 species, taken at 14 stations situated in subtropical montane forests over four-month period, assess across three distinct scales: short-term periodic, diel, hourly. For periodic analysis, employed hierarchical clustering analysis (HCA) coefficient variation (CV). Generalized additive models (GAMs) were utilized diel determined average difference values minute hourly analysis. Results We identified significant day species-specific fluctuations. The survey season was divided into five segments; earliest two showed high variability are best avoided surveys. Data days with heavy rain strong winds reduced should excluded Continuous spanning least seven days, extending minimizing variance. Morning chorus effectively capture majority vocalizations, frequent, shorter intervals aligns closely continuous recording outcomes. Conclusions While our findings context-specific, they highlight significance strategic avian monitoring, optimizing resource utilization enhancing breadth efforts.

Язык: Английский

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A novel deep learning‐based bioacoustic approach for identification of look‐alike white‐eye (Zosterops) species traded in wildlife markets

Ibis, Год журнала: 2024, Номер unknown

Опубликована: Фев. 1, 2024

The songbird trade crisis in East and South Asia has been fuelled by high demand, driving many species to the brink of extinction. This driven desire for songbirds as pets, singing competitions prayer animal release led overexploitation numerous introduction spread invasive alien diseases novel environments. ability identify traded efficiently accurately is crucial monitoring bird markets, protecting threatened enforcing wildlife laws. Citizen scientists can make major contributions these conservation efforts but may be constrained difficulties distinguishing ‘look‐alike’ markets. To address this challenge, we developed a deep learning‐based Artificial Intelligence (AI) bioacoustic tool enable citizen end, used three avian vocalization databases access data 15 morphologically similar White‐eye ( Zosterops ) that are commonly Asian Specifically, employed Inception v3 pre‐trained model classify ambient sound (i.e. non‐bird sound) using 448 recordings obtained. We converted into spectrogram image form) eight augmentation methods enhance performance AI neural network through training validation. found recall, precision F1 score increased amount increased, resulting up 91.6% overall accuracy an 88.8% identifying focal species. Through application bioacoustics learning, approach would law enforcement officials prohibited species, making important conservation.

Язык: Английский

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A deep learning model for detecting and classifying multiple marine mammal species from passive acoustic data

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102906 - 102906

Опубликована: Ноя. 22, 2024

Язык: Английский

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