LRM-MVSR: A lightweight birdsong recognition model based on multi-view feature extraction enhancement and spatial relationship capture

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126735 - 126735

Опубликована: Фев. 1, 2025

Язык: Английский

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A novel approach for bird sound classification with cross correlation by denoising with complementary ensemble empirical mode decomposition using B-spline and LSTM features

Applied Acoustics, Год журнала: 2025, Номер 233, С. 110601 - 110601

Опубликована: Фев. 19, 2025

Язык: Английский

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Environmental sound recognition on embedded devices using deep learning: a review

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 15, 2025

Язык: Английский

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A Comprehensive Review on Advancements and Challenges in Audio Classification Through Deep Learning

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 137 - 160

Опубликована: Март 21, 2025

Deep learning-based audio classification has transformed the industry with improved speech recognition, genre identification in music, and ambient sound detection. The article explores various approaches, including model architectures, evaluation metrics, preprocessing techniques. Traditional methods are compared to deep learning techniques, which have enhanced performance. Spectrograms, Mel-Frequency Cepstral Coefficients, Short-Time Fourier Transform discussed as study also evaluates hybrid training methods, data augmentation, transfer for better outcomes. paper emphasises importance of interpretability, stable datasets, real-time processing overcoming challenges classification. It is expected guide future research advancements this field.

Язык: Английский

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Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review

Biology, Год журнала: 2025, Номер 14(5), С. 520 - 520

Опубликована: Май 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Язык: Английский

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Advanced montane bird monitoring using self-supervised learning and transformer on passive acoustic data

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102927 - 102927

Опубликована: Ноя. 1, 2024

Язык: Английский

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A Study on Bird Calls Recognition Method for Overhead Lines Integrating CNN Models

Опубликована: Ноя. 8, 2024

Язык: Английский

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