A multi-stage ensemble framework for classifying pig vocalizations under noisy animal farm environments

Pig vocalizations are important indicators of health and emotional state, offering significant potential for advancing precision livestock farming. Accurate recognition of these vocalization patterns requires robust noise filtering and effective feature extraction. However, existing studies often focus on isolated patterns such as coughs, limiting their practical applicability in real-world settings. This study introduces the Pig Vocalization Multi-stage Classification (PVMC) model, a comprehensive framework designed to detect and classify a wide range of pig vocalizations under diverse farm conditions for assessing health and emotional stress. PVMC adopts a multi-stage approach that integrates cough and scream detection with emotional state classification, providing a holistic analysis of pig vocalizations. The proposed system features: (1) improved robustness across varying vocalization durations and noise levels, (2) customized model architectures optimized for each stage of the pipeline, and (3) an ensemble learning strategy combining Wav2Vec2 and AST (Audio Spectrogram Transformer) to enhance performance and computational efficiency. PVMC achieved a signal-to-noise ratio (SNR) improvement of up to 4.9dB, 95.80% accuracy in vocalization segmentation, 98.88% accuracy in key vocalization classification, and 92.15% accuracy in emotional state detection. Notably, the ensemble method significantly improved overall precision, recall, and F1-score. These results demonstrate the PVMC model's robustness and practical utility as a deployable solution for real-time pig vocalization monitoring, contributing to intelligent, welfare-oriented livestock management systems.