Intelligent auxiliary system for music performance under edge computing and long short-term recurrent neural networks.

ABSTRACT: Music performance action generation can be applied in multiple real-world scenarios as a research hotspot in computer vision and cross-sequence analysis. However, the current generation methods of music performance actions have consistently ignored the connection between music and performance actions, resulting in a strong sense of separation between visual and auditory content. This paper first analyzes the attention mechanism, Recurrent Neural Network (RNN), and long and short-term RNN. The long and short-term RNN is suitable for sequence data with a strong temporal correlation. Based on this, the current learning method is improved. A new model that combines attention mechanisms and long and short-term RNN is proposed, which can generate performance actions based on music beat sequences

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10166492 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature