构建可扩展的牛声学数据集与分析框架，实现智能牧场动物福利实时监测。
Big Data Approaches to Bovine Bioacoustics: A FAIR-Compliant Dataset and Scalable ML Framework for Precision Livestock Welfare
- 基于多农场麦克风阵列采集90小时声学数据，融合增强生成2900样本。
- 提取24个声学特征，实现发情、痛苦、母子交流等行为精准识别。
- 支持实时处理与可复现研究，适合农业AI与可持续养殖开发者使用。
物联网传感、边缘计算与机器学习的融合正在重塑精准畜牧养殖。然而，生物声学数据因计算复杂性和生态有效性挑战而未被充分利用。本文构建了迄今最全面的牛类发声数据集之一，包含569段经筛选的音频片段，涵盖48种行为类别，来自三家商业化奶牛场，通过多麦克风阵列采集，并经领域知识引导的增强扩展至2900个样本。该数据集符合FAIR原则，应对大数据四大挑战：体量（90小时录音，65.6 GB）、多样性（跨农场与多区域声学环境）、速度（实时处理）与真实性（抗噪特征提取）。分布式处理框架集成iZotope RX先进降噪、音视频同步对齐，以及基于Praat、librosa、openSMILE的标准声学特征工程。初步基准测试揭示发情检测、痛苦分类和母子交流在声学模式上的显著差异。数据集反映真实牛舍声学环境，非受控场景，具备直接部署潜力。本工作为以动物为中心的AI奠定基础，使生物声学数据可用于工业级连续、无创福利评估。通过发布标准化流程与详细元数据，推动可复现研究，连接大数据分析、可持续农业与精准畜牧管理。该框架支持联合国可持续发展目标9，展示数据科学如何将传统农业转型为智能、福利优化的系统，在满足全球粮食需求的同时保障动物伦理关怀。
原文摘要 · Abstract (English)
The convergence of IoT sensing, edge computing, and machine learning is transforming precision livestock farming. Yet bioacoustic data streams remain underused because of computational complexity and ecological validity challenges. We present one of the most comprehensive bovine vocalization datasets to date, with 569 curated clips covering 48 behavioral classes, recorded across three commercial dairy farms using multiple microphone arrays and expanded to 2900 samples through domain informed augmentation. This FAIR compliant resource addresses major Big Data challenges - volume (90 hours of recordings, 65.6 GB), variety (multi farm and multi zone acoustics), velocity (real time processing), and veracity (noise robust feature extraction). Our distributed processing framework integrates advanced denoising using iZotope RX, multimodal synchronization through audio and video alignment, and standardized feature engineering with 24 acoustic descriptors generated from Praat, librosa, and openSMILE. Preliminary benchmarks reveal distinct class level acoustic patterns for estrus detection, distress classification, and maternal communication. The datasets ecological realism, reflecting authentic barn acoustics rather than controlled settings, ensures readiness for field deployment. This work establishes a foundation for animal centered AI, where bioacoustic data enable continuous and non invasive welfare assessment at industrial scale. By releasing standardized pipelines and detailed metadata, we promote reproducible research that connects Big Data analytics, sustainable agriculture, and precision livestock management. The framework supports UN SDG 9, showing how data science can turn traditional farming into intelligent, welfare optimized systems that meet global food needs while upholding ethical animal care.
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