Abstract The modern poultry industry is large-scale and breeding-intensive, making the spread of disease in poultry easier, faster and more harmful. Avian influenza (AI) is the most important disease in poultry, and prevention and detection of avian influenza in poultry is a focus of scientific research and the poultry industry. In this paper, a new sound recognition method, the chicken sound convolutional neural network (CSCNN), is proposed for detection of chickens with avian influenza. According to the spectral differences in environmental noise, chicken behaviour noise and chicken sound, a method was designed to extract the chicken sound from complex sound data. Four features of the chicken sounds were calculated and combined into feature maps, including Logfbank, Mel Frequency Cepstrum Coefficient (MFCC), MFCC Delta and MFCC Delta-Delta. Finally, the sounds of healthy chickens and chickens with avian influenza were recognized using CSCNN. In the experiment, the recognition accuracies of CSCNN via spectrogram (CSCNN-S) were 93.01%, 95.05%, and 97.43% on the 2nd, 4th, and 6th day after injection with the H9N2 virus, and the recognition accuracies of CSCNN with feature mapping (CSCNN-F) were 89.79%, 93.56%, and 95.84%, respectively. The experimental results show that the method proposed in this paper can be used to quickly and effectively detect avian influenza-infected chickens via chicken sound.

中文翻译：

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基于鸡声卷积神经网络的禽流感感染鸡检测

摘要 现代家禽业规模化、养殖密集型，使得疾病在家禽中的传播更容易、更快、危害更大。禽流感（AI）是家禽最重要的疾病，预防和检测家禽禽流感是科学研究和养禽业的重点。在本文中，提出了一种新的声音识别方法——鸡声卷积神经网络（CSCNN），用于检测禽流感鸡。根据环境噪声、鸡行为噪声和鸡声的频谱差异，设计了一种从复杂声音数据中提取鸡声的方法。计算鸡声音的四个特征并组合成特征图，包括Logfbank、Mel Frequency Cepstrum Coefficient (MFCC)、MFCC Delta和MFCC Delta-Delta。最后，使用CSCNN识别健康鸡和禽流感鸡的声音。实验中，注射H9N2病毒后第2天、第4天和第6天，CSCNN通过频谱图（CSCNN-S）的识别准确率分别为93.01%、95.05%和97.43%，具有特征的CSCNN识别准确率在映射 (CSCNN-F) 分别为 89.79%、93.56% 和 95.84%。实验结果表明，本文提出的方法可以通过鸡声快速有效地检测感染禽流感的鸡群。CSCNN with feature mapping（CSCNN-F）的识别准确率分别为89.79%、93.56%和95.84%。实验结果表明，本文提出的方法可以通过鸡声快速有效地检测感染禽流感的鸡群。CSCNN with feature mapping（CSCNN-F）的识别准确率分别为89.79%、93.56%和95.84%。实验结果表明，本文提出的方法可以通过鸡声快速有效地检测感染禽流感的鸡群。