Diagnosis of abnormal sound in loudspeakers by integrated attention mechanism convolutional neural network
In response to the problem of the non-linear,non-stationary nature of speaker abnor-mal sound,as well as their susceptibility to external noise interference,and the low recognition rates,a speaker abnormal sound classification method with variational mode decomposition(VMD)and ID convolutional recurrent attention network(1DCNN-BiLSTM-Attention)was pro-posed.Firstly,different types of abnormal sound signals were collected,and VMD was used to decompose the signals and extract the features of speaker abnormal sound,constructing labeled initial data.Secondly,the feature data was input into the 1DCNN-BiLSTM network for initial feature extraction.The attention mechanism was employed to adaptively optimize the network's learning weights for abnormal sound features,enhancing the networks discriminative capability.Additionally,dropout was optimized to suppress overfitting during the training process,resulting in the construction of the 1DCNN-BiLSTM-Attention classification network.Finally,the pro-posed method was applied to speaker abnormal sound classification.The experimental results demonstrate that this method effectively extracts key features from speaker abnormal sounds,with an average accuracy of 99.17％.Compared to VGG16,RF,and DCNN,the accuracy has been improved by 13.14％,0.56％,and 12.34％respectively.
国家科技期刊平台
NSTL
万方数据
维普
