The field of music generation and analysis is rapidly evolving, with a focus on developing more sophisticated and expressive models. Recent research has explored the use of multi-modal inputs, such as images and text, to generate music that is semantically consistent and perceptually natural. Additionally, there is a growing interest in detecting synthetic music and evaluating the quality of generated music. Noteworthy papers in this area include Art2Music, which proposes a lightweight cross-modal framework for generating music from artistic images and user comments, and Melody or Machine, which introduces a novel dual-stream detection architecture for detecting synthetic music. Other notable works include Story2MIDI, which generates emotion-aligned music from text, and Pianist Transformer, which achieves state-of-the-art performance in expressive piano performance rendering via scalable self-supervised pre-training. These advancements have the potential to revolutionize the field of music generation and analysis, enabling the creation of more realistic and engaging music experiences.