Research on loudspeaker abnormal sound classification based on adaptive variational mode decomposition and RCNN-3
2023
ZHOU Jinglei | HE Jiachen | WANG Xiaoming | CUI Lin
Aimed at the low accuracy of loudspeaker abnormal sound classification, an abnormal sound classification method based on the combination of adaptive variational mode decomposition (AVMD) and residual convolutional neural network (RCNN) is proposed. In AVMD, the grey wolf optimizer (GWO) algorithm was used to optimize the quadratic penalty factor (α) and modal decomposition number (K) of the variational mode decomposition (VMD), and then the VMD was used to extract the features. Finally, the random forest recursive feature elimination (RF-RFE) algorithm was used to extract the optimal feature data, and residual network (ResNet) was introduced by RCNN-3 to classify abnormal sound based on convolutional neural network (CNN). The experimental results show that the proposed method has better classification accuracy and stability, and its average classification accuracy can reach 99.3%.
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