CNN-GRU和SSA-VMD在扬声器异常声分类中的应用

Application of CNN-GRU and SSA-VMD in loudspeaker abnormal sound classification

为了提高扬声器异常声分类的平均准确率,提出用卷积神经网络加门控循环单元(convolutional neural network plus gated recurrent unit,CNN-GRU)和麻雀搜索算法优化变分模态分解(sparrow search algorithm optimization variational modal decomposition,SSA-VMD)模型进行扬声器异常声分类。在特征提取方面,用SSA-VMD模型,确定VMD中二次惩罚因子(α)和模态分解数(k)的最优取值问题,借此提高特征提取精度,减少提取时间,最后再利用VMD提取扬声器响应信号的特征;在分类网络方面,用CNN-GRU网络来进行扬声器异常声分类,以CNN为基础特征提取网络,再用GRU网络进行更深层特征提取,达到提高扬声器平均分类准确率的目标。试验结果表明,经SSA-VMD模型优化参数后,VMD可以更有效提取特征,且分解时间缩短59.8%;CNN-GRU模型具有更高和更稳定的识别率,其平均分类准确率为99.2%。

In order to improve the average accuracy of loudspeaker abnormal sound classification,a convolutional neural network plus gated current unit(CNN-GRU)and sparrow search algorithm optimization variational modal decomposition(SSA-VMD)model was proposed to classify loudspeaker abnormal sound.In the aspect of feature extraction,the SSA-VMD model was used to determine the optimal value of the second penalty factor(α)and modal decomposition number(k)in VMD,so as to improve the accuracy of feature extraction and reduce the extraction time.Finally,the VMD was used to extract the characteristics of the loudspeaker response signal.In terms of classification network,the CNN-GRU network was used to classify the abnormal sound of loudspeakers,the CNN-based feature extraction network was used,and the GRU network was used for deeper feature extraction to achieve the goal of improving the average classification accuracy of loudspeakers.The experimental results show that after optimizing the parameters of SSA-VMD model,VMD can extract features more effectively,and the decomposition time was reduced by 59.8%.The CNN-GRU model has a higher and more stable recognition rate,with an average classification accuracy of 99.2%.