【摘要】 声音与畜牧健康养殖具有密切关联，但由于数据采集难度大等原因，对动物声音的研究还具有较大挑战。以我国特有的德州驴为研究对象，结合通道注意力机制的SE-ResNet18模型提出了一种德州驴声音分类算法。首先，采用谱减法和单参数双门限端点检测法对采集到的三类状态下驴的声音信号进行预处理，并提取13维梅尔倒谱系数为特征。其次，将SE模块集成到修改参数的ResNet18模型中，构建SE-ResNet18模型，其在2.75 MB模型权重大小的基础上平均准确率达到0.940 4，表明该算法在德州驴声音分类的准确率和模型轻量化方面具有良好性能。更多还原

【Abstract】 Sound is closely related to healthy animal husbandry, but due to the difficulty of data collection and other reasons, there are still significant challenges in studying animal sound. In this paper, we take the unique Dezhou donkey in China as the research object, and propose a Dezhou donkey sound classification algorithm based on the SE-ResNet18 model that integrates channel attention mechanism. Firstly, spectral subtraction and single parameter dual threshold endpoint detection method are used to preprocess the collected sound signals of donkeys in three different states, and 13 dimensional Mel frequency cepstral coefficients are extracted as features. Secondly,integrate the SE module into the ResNet18 model with modified parameters to construct the SE-ResNet18 model.SE-ResNet18, with an average accuracy of 0.940 4 and a model weight size of 2.75 MB, demonstrates good performance in both accuracy and model lightweighting for Dezhou donkey sound classification.更多还原