Wood quality of Chinese zither panel based on convolutional neural network and near-infrared spectroscopy.

Abstract

Currently, the wood grade used for Chinese zither panels is mainly manually determined. This method discriminates slowly and is subject to subjective influences, which cannot meet the requirements of mass production in the musical instrument market. This paper proposes a method by combining a convolutional neural network (CNN) and near-infrared spectroscopy to determine wood quality. First, the Savitzky-Golay second derivatization method is used to denoise raw data. Then kernel principal component analysis is used to reduce the dimensionality of spectral data. Then the obtained variables are sent to the proposed one-dimensional CNN model. The model introduces L2 regularization and the multi-channel convolution kernel strategy. The model is then determined by seeking the optimal convolution kernel size. Finally, the test samples are sent to the proposed CNN model to verify the performance of the model. The correct classification accuracy of the test set is 93.9%. Our model has a strong learning ability and a high robustness. The result shows that the proposed method can effectively identify different grades of Chinese zither panel wood.