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根据声学信号来识别幼的性别

J J Yin¹, W G Li¹, Y F Liu¹

¹College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China.

Poultry science

|April 23, 2024

概括

这项研究引入了一种声学方法,用于使用深度学习来识别子的性别. 新的Res2Net算法实现了高精度,为家禽养殖提供了具有成本效益的解决方案.

科学领域:

禽畜科学禽畜科学禽畜科学
生物声学是一种生物声学.
机器学习机器学习

背景情况:

在幼中准确的性别识别对于高效的家禽养殖至关重要,影响繁殖精度和经济可行性.
目前用于子性别识别的方法可能是劳动密集型的,可能缺乏一致的准确性.

研究的目的:

开发和评估基于声信号的自动化方法来识别子的性别.
为了提高年轻子的性别识别的准确性和效率.

主要方法:

用光谱减法和高通过来减少噪音,先处理了子的发声.
使用基于短时间能量 (STE),零交叉率 (ZCR) 和持续时间 (D) 的双门终点检测方法检测声音.
通过使用增强的Res2Net深度学习模型提取和分类Mel-Spectrogram特征,其中包括Squeeze-and-Excitation (SE) 注意力和Ghost模块.

主要成果:

增强的Res2Net模型实现了94.80%的准确性,回忆率为94.92%和特异性为94.69%.
挤压和刺激 (SE) 注意力机制提高了2.01%的模型准确性,而幽灵模块则减少了7.26M的参数和0.85G的FLOP.
最后的模型,结合发声检测得分和平均信心,达到96.67%的整体准确度,用于预测个体子的性别.

结论:

拟议的基于声学的方法有效地识别了高精度和成本效益的子性别.
这种方法为家禽行业的自动化性别识别系统提供了有希望的参考.
集成SE注意力和幽灵模块显著优化了深度学习模型的性能和效率.

关键词:

声学特征声学特征声学特征动物福利动物福利深度学习是一种深度学习.小子的小子性别识别性别识别

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