Abstract
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.
Abstract (translated)
通过多源数据融合理解动物的发声对于在精准畜牧业中评估情绪状态和提高动物福利至关重要。本研究旨在通过运用多模态数据融合技术解码奶牛叫声，整合转录、语义分析、情境与情感评估以及声学特征提取。我们利用自然语言处理模型将奶牛叫声的音频记录转换为书面形式。通过结合多种声学特征（频率、持续时间和强度）与转录的文字数据，我们开发了对奶牛叫声的全面表示。在自定义开发的本体论中使用数据融合，我们将叫声分类为高频呼叫（与压力或唤醒状态相关）和低频呼叫（与满足感或平静情绪相关）。通过分析融合的多维数据，我们识别出表明情感困扰的具体频率测量和声音光谱结果等焦虑相关特征。评估20头单独奶牛叫声的情感和声学特征使我们能够确定叫唤模式和情感状态之间的差异。利用先进的机器学习算法——随机森林、支持向量机和循环神经网络，我们有效地处理并融合了多源数据以分类奶牛叫声。这些模型经过优化，可以应对实际农场环境中固有的计算需求和数据质量挑战。我们的研究结果表明，在动物福利监测中使用多源数据融合与智能处理技术的有效性。本研究表明在动物福利评估方面的一个重大进展，强调创新融合技术在理解和改善奶牛情感福祉中的作用。
URL
https://arxiv.org/abs/2411.00477
