Audio recognition of Chinese traditional instruments based on machine learning - Rongfeng Li; Qin Zhang (Cognitive Computation and Systems, 2022)
Abstract This paper is part of a special issue on Music Technology. We study the type recognition of traditional Chinese musical instrument audio in the common way. Using MEL spectrum characteristics as input, we train an 8‐layer convolutional neural network, and finally achieve 99.3% accuracy. After that, this paper mainly studies the performance skill recognition of Chinese traditional musical instruments. Firstly, for a single instrument, the features were extracted by using the pre‐trained ResNet model, and then the SVM algorithm was used to classify all the instruments with an accuracy of 99%. Then, in order to improve the generalization of the model, the paper proposes the performance skill recognition of the same kind of instruments. In this way, the regularity of the same playing technique of different instruments can be utilized. Finally, the recognition accuracy of the four kinds of instruments is as follows: 95.7% for blowing instruments, 82.2% for plucked‐string instruments, 88.3% for strings instruments, and 97.5% for percussion instruments. We open source the audio database of traditional Chinese musical instruments and the Python source code of the whole experiment for further research.