Research Based on the Application and Exploration of Artificial Intelligence in the Field of Traditional Music

Abstract

Traditional Chinese music has undergone trials and tribulations. To date, traditional music has been gradually improved, preserved, and passed down, both in terms of theoretical works and traditional music varieties. However, the current state of traditional music is still a cause for concern. Whether it is scholars engaged in the study of traditional music, universities, or local government agencies, there are still areas that need to be improved individually. The need to cultivate a future audience for traditional music and to make full use of new media such as the Internet is a priority at this stage. The emergence of any new technology is bound to have some impact on the existing social system, and the emergence of artificial intelligence is no exception. Due to the limitation of technology, only a small number of basic applications have been developed, and it is the future mission of research workers whether they can develop more advanced products for music lovers to experience on the basis of ensuring the basic maturity of these applications. In this paper, we introduce the convolutional deep belief network model based on the restricted Boltzmann machine and apply the convolutional deep belief network algorithm to the music melody recognition. Firstly, it was pretrained by an unsupervised greedy layer-by-layer algorithm. Then, the network parameters were fine-tuned by a supervised network training method, and the recognition ability of the model was improved by adjusting the network parameters. The experimental results show that the recognition effect of the system is more obvious under the condition that the length of music samples is 3 s, and the recognition effect is better when the number of model layers is 2 than that when the number of layers is 1.