Decoding Poultry Vocalizations -- Natural Language Processing and Transformer Models for Semantic and Emotional Analysis

Deciphering the acoustic language of chickens offers new opportunities in animal welfare and ecological informatics. Their subtle vocal signals encode health conditions, emotional states, and dynamic interactions within ecosystems. Understanding the semantics of these calls provides a valuable tool for interpreting their functional vocabulary and clarifying how each sound serves a specific purpose in social and environmental contexts. We apply advanced Natural Language Processing and transformer based models to translate bioacoustic data into meaningful insights. Our method integrates Wave2Vec 2.0 for raw audio feature extraction with a fine tuned Bidirectional Encoder Representations from Transformers model, pretrained on a broad corpus of animal sounds and adapted to poultry tasks. This pipeline decodes poultry vocalizations into interpretable categories including distress calls, feeding signals, and mating vocalizations, revealing emotional nuances often overlooked by conventional analyses. Achieving 92 percent accuracy in classifying key vocalization types, our approach demonstrates the feasibility of real time automated monitoring of flock health and stress. By tracking this functional vocabulary, farmers can respond proactively to environmental or behavioral changes, improving poultry welfare, reducing stress related productivity losses, and supporting more sustainable farm management. Beyond agriculture, this research enhances our understanding of computational ecology. Accessing the semantic foundation of animal calls may indicate biodiversity, environmental stressors, and species interactions, informing integrative ecosystem level decision making.

Mapping Methane -- The Impact of Dairy Farm Practices on Emissions Through Satellite Data and Machine Learning

This study investigates the correlation between dairy farm characteristics and methane concentrations as derived from satellite observations in Eastern Canada. Utilizing data from 11 dairy farms collected between January 2020 and December 2022, we integrated Sentinel-5P satellite methane data with critical farm-level attributes, including herd genetics, feeding practices, and management strategies. Initial analyses revealed significant correlations with methane concentrations, leading to the application of Variance Inflation Factor (VIF) and Principal Component Analysis (PCA) to address multicollinearity and enhance model stability. Subsequently, machine learning models - specifically Random Forest and Neural Networks - were employed to evaluate feature importance and predict methane emissions. Our findings indicate a strong negative correlation between the Estimated Breeding Value (EBV) for protein percentage and methane concentrations, suggesting that genetic selection for higher milk protein content could be an effective strategy for emissions reduction. The integration of atmospheric transport models with satellite data further refined our emission estimates, significantly enhancing accuracy and spatial resolution. This research underscores the potential of advanced satellite monitoring, machine learning techniques, and atmospheric modeling in improving methane emission assessments within the dairy sector. It emphasizes the critical role of farm-specific characteristics in developing effective mitigation strategies. Future investigations should focus on expanding the dataset and incorporating inversion modeling for more precise emission quantification. Balancing ecological impacts with economic viability will be essential for fostering sustainable dairy farming practices.

Multi Modal Information Fusion of Acoustic and Linguistic Data for Decoding Dairy Cow Vocalizations in Animal Welfare Assessment

Understanding animal vocalizations through multi-source data fusion is crucial for assessing emotional states and enhancing animal welfare in precision livestock farming. This study aims to decode dairy cow contact calls by employing multi-modal data fusion techniques, integrating transcription, semantic analysis, contextual and emotional assessment, and acoustic feature extraction. We utilized the Natural Language Processing model to transcribe audio recordings of cow vocalizations into written form. By fusing multiple acoustic features frequency, duration, and intensity with transcribed textual data, we developed a comprehensive representation of cow vocalizations. Utilizing data fusion within a custom-developed ontology, we categorized vocalizations into high frequency calls associated with distress or arousal, and low frequency calls linked to contentment or calmness. Analyzing the fused multi dimensional data, we identified anxiety related features indicative of emotional distress, including specific frequency measurements and sound spectrum results. Assessing the sentiment and acoustic features of vocalizations from 20 individual cows allowed us to determine differences in calling patterns and emotional states. Employing advanced machine learning algorithms, Random Forest, Support Vector Machine, and Recurrent Neural Networks, we effectively processed and fused multi-source data to classify cow vocalizations. These models were optimized to handle computational demands and data quality challenges inherent in practical farm environments. Our findings demonstrate the effectiveness of multi-source data fusion and intelligent processing techniques in animal welfare monitoring. This study represents a significant advancement in animal welfare assessment, highlighting the role of innovative fusion technologies in understanding and improving the emotional wellbeing of dairy cows.