The field of music information retrieval and generation is rapidly evolving, with a focus on developing innovative methods for music modeling, transcription, and generation. Recent research has explored the use of deep learning techniques, such as transformer architectures and graph neural networks, to improve music analysis and generation tasks. Notably, there is a growing interest in developing models that can learn from large-scale datasets and generate high-quality music samples. Additionally, researchers are working on improving music transcription accuracy, particularly in the context of child-centered audio recordings and automatic lyrics transcription.

Some particularly noteworthy papers in this area include the correlation-permutation approach for speech-music encoders model merging, which enables the creation of unified audio models from independently trained encoders. The LiLAC model is also notable, as it offers a lightweight and modular architecture for musical audio generation with fine-grained controls. Other papers, such as the ones introducing the Fretting-Transformer and SonicVerse models, demonstrate significant advancements in music transcription and captioning tasks.