卷积神经网络在乐器板材优劣识别中的应用研究.

At present,the vibration signal recognition algorithm for national musical instrument plate has the shortcomings of complex feature extraction and time-consuming. To solve this problem,this paper proposed a classification algorithm of wood vibration signal based on convolution neural network,to identify the quality of the musical instrument. Convolution neural network combined feature extraction and classification process to train the neural network, which owned the advantages of high recognition rate and good robustness. Firstly,this paper mainly analyzed and discussed spectrogram characteristics of the extraction of wood vibration signals. Then combining convolution neural network and grid search method, it could adjust the parameters. In order to avoid over-fitting,it obtained the final classification results by using new technologies such as ReLU and dropout. The experiments show that the accuracy of the test sample reaches 96% , which is obviously better than the traditional method. This method can reduce the error of manual measurement and speed up the selection time of the plate,and provide a more convenient method for the selection of the national musical instrument manufacturing field. [ABSTRACT FROM AUTHOR]