ArticleSensors (Basel, Switzerland)2025
Decoding Poultry Welfare from Sound-A Machine Learning Framework for Non-Invasive Acoustic Monitoring.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Smart Animal Welfare: A Review of Sensing Technologies, Deployment Challenges, and AI-Driven Insights.Sensors (Basel, Switzerland) · 2026Review
- Two-stage acoustic detection and fine-grained classification of laying-hen vocalizations for heat-stress monitoring.Poultry science · 2026Article
- Chicken disease detection and localization using multi-noise separation and acoustic recognition.Poultry science · 2026Article
- Pilot Room-Level Acoustic and Physiological Monitoring of Respiratory Disturbance in Pigs Following ExperimentalVeterinary sciences · 2026Article
- Sensor-Based Precision Feeding Systems in Animal Production: Technologies and Applications.Animals : an open access journal from MDPI · 2026Review
- Multimodal digital sensing of early-life laying hens: a pilot study integrating thermal, acoustic, optical-flow and environmental data.Frontiers in veterinary science · 2026Article
- A longitudinal multimodal big data infrastructure for precision poultry monitoring.Frontiers in big data · 2026Article
- Decoding laying hen behavior and physiological status through acoustic biomarkers: temporal patterns, rooster-hen vocalization identification in group housing and environmental adaptation.Poultry science · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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 stress. This study proposes an integrated analytical framework that combines signal-level statistical analysis with machine learning and deep learning classifiers to interpret chicken vocalizations in a welfare assessment context. The framework was evaluated using three complementary datasets encompassing health-related vocalizations, behavioral call types, and stress-induced acoustic responses. The pipeline employs a multistage process comprising high-fidelity signal acquisition, feature extraction (e.g., mel-frequency cepstral coefficients, spectral contrast, zero-crossing rate), and classification using models including Random Forest, HistGradientBoosting, CatBoost, TabNet, and LSTM. Feature importance analysis and statistical tests (e.g.,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.