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一个多阶段的整体框架,用于在杂的动物养殖场环境下对猪发声进行分类
Seyeon Chung1, Heng Zhou1,2, Dewa Made Sri Arsa3
1Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, 54896, Republic of Korea.
Scientific reports
|October 6, 2025
概括
本研究介绍了用于猪健康监测的猪口音多阶段分类 (PVMC) 模型. 该PVMC模型准确地检测和分类各种猪声调,提高精确畜牧业和动物福利.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 生物声学是一种生物声学.
背景情况:
- 猪的发声是动物健康和情绪状态的关键指标.
- 精密畜牧业需要对这些发音进行准确的分析.
- 现有的方法与噪音作斗争,并对各种发音进行分类.
研究的目的:
- 开发一个全面的框架来检测和分类各种各样的猪发声.
- 评估真实世界农场条件下的猪健康和情绪压力.
- 提高猪发声分析的实际应用性.
主要方法:
- 引入了猪声调多阶段分类 (PVMC) 模型.
- 综合咳/尖叫检测与情绪状态分类.
- 采用组合学习策略,将Wav2Vec2和音频谱变压器 (AST) 模型结合起来.
主要成果:
- 实现了高达4.9dB的SNR改进和95.80%的声音细分精度.
- 在关键语音分类方面达到98.88%的准确性,在情绪状态检测方面达到92.15%.
- 组合学习显著提高了精度,回忆和F1分数.
结论:
- 该PVMC模型证明了对实时猪声调监控的稳定性和实际实用性.
- PVMC有助于智能化,以福利为导向的牲畜管理系统.
- 该模型的多阶段方法和集体学习提高了在各种农场条件下的性能.
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