AIMC Topic: Vocalization, Animal

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Optimal feature selection and model explanation for reef fish sound classification.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Fish produce a wide variety of sounds that contribute to the soundscapes of aquatic environments. In reef systems, these sounds are important acoustic cues for various ecological processes. Artificial intelligence methods to detect, classify and iden...

Acoustic monitoring for tropical insect conservation.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Monitoring the species-specific sounds produced by insects could provide us with a rapid, reliable, non-invasive measure of tropical ecosystem health and biodiversity. Although acoustic biodiversity monitoring has made rapid progress over the past de...

Environmental drivers of calling activity in the critically endangered lemur leaf frog, (Hylidae: Phyllomedusinae).

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Tropical frog species are known to exhibit high sensitivity to weather regime alterations, which leaves them vulnerable to ongoing climate change. This challenge is exacerbated by limited knowledge of species-specific responses to environmental chang...

Decoding Poultry Welfare from Sound-A Machine Learning Framework for Non-Invasive Acoustic Monitoring.

Sensors (Basel, Switzerland)
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental str...

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...