Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies

Sensors, Год журнала: 2024, Номер 24(24), С. 7978 - 7978

Опубликована: Дек. 13, 2024

Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding behavior and environmental interactions. Traditional methods, which involve manual review extensive recordings, pose significant challenges. This study proposes an automated system detecting classifying vocalizations, enhancing efficiency analysis. The uses preprocessing step to segment relevant sound regions audio followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), linear-frequency (LFCCs). These features input into convolutional neural network (CNN) classifiers evaluate performance. Experimental results demonstrate the effectiveness different CNN models with AlexNet, DenseNet, EfficientNet, ResNet50, ResNet152 being evaluated. achieves high accuracy vocal behaviors, such barking howling dogs, providing robust tool highlights importance systems bioacoustics suggests future improvements deep learning-based methods enhanced classification

Язык: Английский

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The Convergence of AI and animal-inspired robots for ecological conservation

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102950 - 102950

Опубликована: Дек. 1, 2024

Язык: Английский

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Assessment of pig welfare at slaughterhouse level: A systematic review of animal-based indicators suitable for inclusion in monitoring protocols

Meat Science, Год журнала: 2024, Номер 220, С. 109689 - 109689

Опубликована: Окт. 19, 2024

Язык: Английский

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Acoustic-based Models to Assess Herd-level Calves' Emotional State: A Machine Learning Approach

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100682 - 100682

Опубликована: Ноя. 1, 2024

Язык: Английский

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Unsupervised Domain Adaptation for Mitigating Sensor Variability and Interspecies Heterogeneity in Animal Activity Recognition

Animals, Год журнала: 2023, Номер 13(20), С. 3276 - 3276

Опубликована: Окт. 20, 2023

Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability difficulty obtaining labeled datasets. To address this issue, study intensively investigates impact unsupervised adaptation (UDA) for AAR. We compared three distinct types UDA techniques: minimizing divergence-based, adversarial-based, reconstruction-based approaches. By leveraging UDA, classifiers enable model learn domain-invariant features, allowing trained on source perform well target without labels. evaluated effectiveness techniques dog movement additional from horses. The application across positions (neck back), sizes (middle-sized large-sized), gender (female male) within data, as species (dog horses), exhibits improvements classification performance reduced discrepancy. results highlight potential mitigate shift enhance various settings different species, providing valuable insights practical real-world scenarios where is scarce.

Язык: Английский

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Enhancing Veterinary Behavior Research: Evidence-Based Strategies for Overcoming the Limitations of Underpowered Studies

Journal of Veterinary Behavior, Год журнала: 2024, Номер 71, С. A3 - A5

Опубликована: Янв. 1, 2024

Язык: Английский

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Automated Prediction of Spawning Nights Using Machine Learning Analysis of Flatfish Behaviour

Опубликована: Янв. 1, 2024

Язык: Английский

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Maximum vertical height during wing flapping of laying hens captured with a depth camera

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 14, 2024

Abstract Cage-free housing systems for laying hens, and their accompanying guidelines, legislation, audits, are becoming more common around the world. regulations often specify requirements floor space cage height, but availability of three-dimensional can vary depending on system configurations. Little research has looked at how much vertical a hen occupies while flapping her wings, which is arguably most space-intensive behavior. Therefore, objective this study was to use depth sensing camera measure maximum height hens reach when wing without physical obstructions. Twenty-eight individually caged Hy-line W36 45 weeks age were evaluated. A ceiling-mounted centered above test pen calibrated prior collecting data. During testing, one time placed in recorded wings. From footage, minimum distance between pixels obtained each frame, we computed reached by hen. Results used during event showed that 51.0 ± 4.7 cm. No measures correlated with from (P>0.05). Hens single strain, old enough have keel damage, cage-reared housed, preventing us generalizing results too far. However, cameras provide useful approach varying strains, ages, rearing/housing methods need perform dynamic behaviors.

Язык: Английский

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Tracking puppy development: automated analysis and qualitative behavioral assessment in repeated open field tests

Veterinary Research Communications, Год журнала: 2024, Номер 49(1)

Опубликована: Ноя. 25, 2024

Язык: Английский

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Tag 'n' Track: Tackling the validation challenge in animal behaviour studies through automated referencing with ArUco markers

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 229, С. 109812 - 109812

Опубликована: Дек. 31, 2024

Язык: Английский

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