핵심 개념
The author introduces the M2UGen model, utilizing large language models for music understanding and multi-modal music generation. The approach aims to enhance user experience in music-related artistic creation.
초록
The M2UGen framework integrates large language models to comprehend and generate music across different modalities. It addresses the gap in research combining understanding and generation tasks using LLMs. The model achieves or surpasses state-of-the-art performance in various subtasks such as music question answering, text/image/video-to-music generation, and music editing.
Key points:
- Introduction of M2UGen framework for multi-modal music understanding and generation.
- Utilization of large language models for enhanced comprehension and creativity in music tasks.
- Evaluation of the model's performance across different subtasks showcasing superiority over existing models.
- Comprehensive methodology for generating datasets to train the M2UGen model.
- Future work focuses on fine-grained music understanding enhancement and improving correlation between generated music and input instructions.
통계
"The MU-Caps dataset comprises approximately 1,200 hours of music sourced from AudioSet."
"MUEdit dataset includes 55.69 hours of 10-second music pairs."
"MUImage dataset is assembled by obtaining music samples from AudioSet with paired videos."
"MUVideo dataset is curated by gathering music samples from AudioSet with corresponding videos."