AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring

Abstract

Artificial intelligence and bioacoustics represent a paradigm shift in non-invasive poultry welfare monitoring through advanced vocalization analysis. This comprehensive systematic review critically examines the transformative evolution from traditional acoustic feature extraction-including Mel-Frequency Cepstral Coefficients (MFCCs), spectral entropy, and spectrograms-to cutting-edge deep learning architectures encompassing Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, and groundbreaking self-supervised models such as wav2vec2 and Whisper. The investigation reveals compelling evidence for edge computing deployment via TinyML frameworks, addressing critical scalability challenges in commercial poultry environments characterized by acoustic complexity and computational constraints. Advanced applications spanning emotion recognition, disease detection, and behavioral phenotyping demonstrate unprecedented potential for real-time welfare assessment. Through rigorous bibliometric co-occurrence mapping and thematic clustering analysis, this review exposes persistent methodological bottlenecks: dataset standardization deficits, evaluation protocol inconsistencies, and algorithmic interpretability limitations. Critical knowledge gaps emerge in cross-species domain generalization and contextual acoustic adaptation, demanding urgent research prioritization. The findings underscore explainable AI integration as essential for establishing stakeholder trust and regulatory compliance in automated welfare monitoring systems. This synthesis positions acoustic AI as a cornerstone technology enabling ethical, transparent, and scientifically robust precision livestock farming, bridging computational innovation with biological relevance for sustainable poultry production systems. Future research directions emphasize multi-modal sensor integration, standardized evaluation frameworks, and domain-adaptive models capable of generalizing across diverse poultry breeds, housing conditions, and environmental contexts while maintaining interpretability for practical farm deployment.

Keywords: TinyML; acoustic monitoring; animal welfare; bioacoustics classification; edge AI; poultry vocalization.

Figure 1

Systematic review pipeline outlining database search, screening, full-text evaluation for on-farm AI acoustic studies, and thematic synthesis from 124 included papers.

Figure 2

Taxonomy of poultry vocalization analysis methods across five categories, including signal processing, classical ML, deep learning, self-supervised learning, and explainable AI.

Figure 3

Workflow of Bioacoustic Analysis: Segmentation to Modeling using Specialized Tools.

Figure 4

Keyword co-occurrence network showing thematic clusters in livestock vocalization research. Node size indicates keyword frequency, while colors represent distinct research themes such as poultry monitoring, acoustic analysis, and deep learning approaches.