AIMC Topic: Vocalization, Animal

Clear Filters Showing 21 to 30 of 85 articles

Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.

Sensors (Basel, Switzerland)
Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous d...

Explainable classification of goat vocalizations using convolutional neural networks.

PloS one
Efficient precision livestock farming relies on having timely access to data and information that accurately describes both the animals and their surrounding environment. This paper advances classification of goat vocalizations leveraging a publicly ...

Automated detection of broiler vocalizations a machine learning approach for broiler chicken vocalization monitoring.

Poultry science
The poultry industry relies on highly efficient production systems. For sustainable food production, where maintaining broiler welfare is crucial, it is essential to have robust data collection systems and automated methods for assessing broiler heal...

Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis.

PloS one
The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock. However, there has been little research ...

Elephant Sound Classification Using Deep Learning Optimization.

Sensors (Basel, Switzerland)
Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation....

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

Sensors (Basel, Switzerland)
Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual revi...

Classifying vocal responses of broilers to environmental stressors via artificial neural network.

Animal : an international journal of animal bioscience
Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transforme...

Deep learning algorithms reveal increased social activity in rats at the onset of the dark phase of the light/dark cycle.

PloS one
The rapid decrease of light intensity is a potent stimulus of rats' activity. The nature of this activity, including the character of social behavior and the composition of concomitant ultrasonic vocalizations (USVs), is unknown. Using deep learning ...

Deep transfer learning-based bird species classification using mel spectrogram images.

PloS one
The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, ...

Applying machine learning to primate bioacoustics: Review and perspectives.

American journal of primatology
This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep ...