The construction of improved GCA multi-style music generation model for music intelligent teaching classroom

Abstract

In order to address the limitations of traditional models in generating music styles, a multi style music generation model has been designed to support music teaching. The main contribution of the research is the introduction of a Multi style chord music generation network to enhance the adaptability and innovative generation ability of the model to different music styles. The weight of different music styles is adjusted through a style transfer mechanism to achieve seamless transition of chord styles. The experimental results show that the loss value of the research method is 0.16, and the accuracy of the model's note recognition is 81.68%, both of which reach a high level. The accuracy, recall, and F1 score of the research method for music sequence recognition are 95.16%, 92.53%, and 0.948, respectively, all of which are better than the comparative models. This indicates that the research method has better flexibility in music generation and stronger ability to generate multi style music. Research can aid with the generation of multi style music in music teaching.