基于多特征融合的猪声调分类方法的研究
Yuting Hou1,2, Qifeng Li1,3, Zuchao Wang2
1Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一种新的方法,用于使用多特征融合和基因算法优化的神经网络来分类猪的发声. 该方法在识别声,声和咳方面取得了很高的准确性,有助于动物福利监测.
科学领域:
- 动物科学动物科学
- 生物声学是一种生物声学.
- 机器学习 机器学习
背景情况:
- 准确的猪声声分类对于监测大型繁殖活动中的动物福利和健康至关重要.
- 现有的方法可能缺乏对猪声信号的细微解释所需的精度.
研究的目的:
- 开发和验证一个强大的猪发声分类方法.
- 通过融合多个声学特征和采用先进的机器学习算法来提高识别精度.
主要方法:
- 提取了短时间能量,频率中心点,形成频率和Mel频率的 cepstral 系数作为融合特征.
- 应用主要组件分析 (PCA) 用于功能改进.
- 构建了一个通过遗传算法优化的反向传播 (BP) 神经网络模型.
主要成果:
- 在猪,尖叫和咳时,获得了93.2%的平均识别准确度.
- 获得的平均识别精度为92.9%,平均回忆率为92.8%.
- 在区分不同类型的猪发声中表现出卓越的表现.
结论:
- 拟议的多功能融合方法显著提高了猪声声分类的准确性.
- 优化基因算法的BP神经网络为猪声信号的自动识别提供了可靠的工具.
- 这种方法为猪发声信息反和自动监控系统提供了宝贵的见解.
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