基于CNN-RNN集成的隧道事故异常声音识别

Tunnel accident abnormal sound recognition based on CNN-RNN integration

为提高公路隧道事故异常声音识别的准确率,并针对卷积神经网络只关注局部信息问题,提出了一种基于CNN-RNN集成的声音识别模型。该模型采用Stacking集成策略将CNN的强特征表达能力和RNN的强记忆能力相结合,并使用门控循环单元减少循环神经网络的计算复杂度,将SIREN正弦周期函数作为RNN的隐式激活函数,增强模型对声音数据的拟合能力,设计多通道卷积细化特征提取的精度,实现全局化特征提取。在异常声音数据集上评估了所提声音识别模型的识别性能,实验结果表明:提出的声音模型的识别性能高于其他模型,且更加稳健,可有效识别公路隧道事故的异常声音。

In order to improve the accuracy of abnormal sound recognition of highway tunnel accident and to solve the problem that convolutional neural networks only pay attention to local information.An integrated voice recognition model based on CNN-RNN is proposed.The model used the Stacking integration strategy to combine the strong feature expression ability of CNN and the strong memory ability of RNN.The gated cyclic memory unit was used to reduce the computational complexity of RNN.SIREN sinusoidal periodic function was used as the implicit activation function of RNN to enhance the fitting ability of the model to sound data.The precision of multi-channel convolution refinement feature extraction was designed to achieve global feature extraction.The performance of the proposed sound recognition model was evaluated on the abnormal sound data set.Experimental results show that the proposed sound model has higher recognition performance than other models and is more robust,which can effectively identify the abnormal sound of highway tunnel accidents.