Abstract:

As an important material for manufacturing resonant components of musical instruments, Paulownia has an important influence on the sound quality of Ruan. In this paper, a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods. Generally, the selection of materials for Ruan manufacturing relies primarily on manually weighing, observing, striking, and listening by the instrument technician. Deficiencies in scientific theory have hindered the quality of the finished Ruan. In this study, nine Ruans were manufactured, and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans. Out of a total of 180 data sets, 145 and 45 sets were chosen for training and validation, respectively. In this paper, typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan. The vibration characteristics of the wood were tested, and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material. The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural network. The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood. When the model-predicted values were compared with the traditional predicted results, it was found that the generalized regression neural network had good performance, achieving an accuracy of 93.8% which was highly consistent with the experimental results. It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Key words: Sound quality, Wood vibration performance, Paulownia wood, Machine learning methods