面向鸟鸣声识别任务的深度学习技术

在生态系统中,鸟类是重要的组成部分,对调节生态环境和监测生物多样性至关重要,甚至可以通过监测鸟群动向与监听鸟群异常鸣声对地震、海啸等自然灾害进行辅助预测和防范,为此,鸟鸣声识别和异常鸣声监测成为热门的研究方向。然而,由于传统鸟鸣声识别方法存在特征提取不充分等问题,导致识别率不高。本文采用融合特征的方法结合深度学习技术提取鸟鸣声特征,融合特征选择改良后的对数梅尔谱差分参数同原始信号参数拼接所得的特征;深度学习方法是基于Dense Net121网络结构,并融入自注意力模块与中心损失函数进行鸟鸣声识别。自注意力模块部分提高了关键通道的特征表达能力;中心损失函数可解决类内特征不紧凑问题。我们通过消融实验对比验证,对在Xeno-Canto世界野生鸟类声音公开数据集上选取的10种鸟类声音进行识别,准确率达到96.9%。代码已开源至Github:https://github.com/Carrie X6/-Xeno-Canto-.git。

Deep learning techniques for bird chirp recognition task

Background: In the ecosystem, birds are an important component, which is crucial for regulating the ecological environment and monitoring biodiversity, and can even assist in predicting natural disasters such as earthquakes and tsunamis by monitoring the movement of birds and listening to their abnormal calls, so bird sound recognition and abnormal call detection have become popular research directions. However, low recognition rate is caused to the problems of insufficient feature extraction in traditional bird sound recognition methods.