卷积神经网络在乐器板材优劣识别中的应用研究

t 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 combines feature extraction and classification process to train the neural network, which owns 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 can adjust the parameters. In order to avoid over-fitting, the final classification results were obtained by using new technologies such as ReLU and Dropout. The experiments showed that the accuracy of the test sample reached 96%, which are 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.