Efficiency of AI Technology Application in Music Education - A Perspective Based on Deep Learning Model DLMM

In recent years, the active attempts and breakthroughs of artificial intelligence in music applications and music education have been amazing. The study proposes a lightweight music score recognition method, CRNN-lite, which achieves both lightweight and improved accuracy. In order that the method can be better and faster migrated to be applied to music education, the article designs a new multimodal domain adaptation algorithm based on differential learning, which effectively utilizes the variability of different modal models for multimodal domain adaptation. Finally, the performance comparison analysis and practical application effects of the proposed method in this paper are discussed. Comprehensive experiments show that the multimodal domain adaptation algorithm DLMM based on differential learning proposed in this paper both achieve better recognition results than other methods, and compared with the original recognition algorithm CRNN-Lite, CRNN-Lite+DLMM precision rises by 2.9%, and the recall rate rises by 1.1%, mAP@0.5 increased by 1.3%.