Exploring the Realm of Automated Music Composition with Models based on Transformer and its Variants

The Transformer architecture and its modelling variants have great potential in the music domain. Traditional approaches, such as rule-based systems and RNNs, have limitations in capturing the complex temporal dependencies and hierarchical structures inherent in music. With the introduction of its self-concern mechanism, the Transformer can effectively address this problem by capturing remote dependencies and allowing parallel processing of sequences. Transformer-based model variants such as Music Transformers, MuseNet and Jukebox have demonstrated that they can generate high-quality, varied, and stylistically rich compositions. Despite the success of these models, some challenges still need to be solved, such as high computational requirements and limited control over musical style and emotion. Possible future research directions include optimizing computational efficiency, enhancing stylistic and emotional control, and developing hybrid models that combine other models with Transformer. This article provides a comprehensive overview of the current state of research and potential and future applications of Transformer for music generation.