The recent advancements in the field of music research have shown a significant shift towards leveraging multi-modal data and innovative generative models to enhance various aspects of music creation, understanding, and interaction. A notable trend is the integration of diffusion models and large language models (LLMs) to address complex tasks such as music-video generation, video-to-music alignment, and multi-modal music understanding and generation. These models are being employed to capture the nuanced and diverse nature of user preferences, enabling more flexible and controllable music discovery and creation processes. Additionally, there is a growing emphasis on the development of tools and frameworks that facilitate the alignment of music with other modalities, such as visual and textual data, to create richer and more synchronized audio-visual experiences. The field is also witnessing advancements in the area of source separation and automatic transcription, with new approaches leveraging deep learning to improve the quality and efficiency of these tasks. Furthermore, there is a focus on designing inclusive and interactive AI systems for music-making, which aim to empower musicians of all abilities to engage in collaborative and creative processes. Overall, the current research landscape is characterized by a blend of technological innovation and a commitment to enhancing the accessibility and expressiveness of music across different domains.