The current landscape of research leveraging large language models (LLMs) is
experiencing a surge. Many works harness the powerful reasoning capabilities of
these models to comprehend various modalities, such as text, speech, images,
videos, etc. They also utilize LLMs to understand human intention and generate
desired outputs like images, videos, and music. However, research that combines
both understanding and generation using LLMs is still limited and in its
nascent stage. To address this gap, we introduce a Multi-modal Music
Understanding and Generation (M$^{2}$UGen) framework that integrates LLM’s
abilities to comprehend and generate music for different modalities. The
M$^{2}$UGen framework is purpose-built to unlock creative potential from
diverse sources of inspiration, encompassing music, image, and video through
the use of pretrained MERT, ViT, and ViViT models, respectively. To enable
music generation, we explore the use of AudioLDM 2 and MusicGen. Bridging
multi-modal understanding and music generation is accomplished through the
integration of the LLaMA 2 model. Furthermore, we make use of the MU-LLaMA
model to generate extensive datasets that support text/image/video-to-music
generation, facilitating the training of our M$^{2}$UGen framework. We conduct
a thorough evaluation of our proposed framework. The experimental results
demonstrate that our model achieves or surpasses the performance of the current
state-of-the-art models.
The Multi-modal Music Understanding and Generation (M$^{2}$UGen) Framework: Advancing Research in Large Language Models
In recent years, research leveraging large language models (LLMs) has gained significant momentum. These models have demonstrated remarkable capabilities in understanding and generating various modalities such as text, speech, images, and videos. However, there is still a gap when it comes to combining understanding and generation using LLMs, especially in the context of music. The M$^{2}$UGen framework aims to bridge this gap by integrating LLMs’ abilities to comprehend and generate music across different modalities.
Multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are all interconnected fields that rely on the integration of different modalities to create immersive and interactive experiences. The M$^{2}$UGen framework embodies the multi-disciplinary nature of these fields by leveraging pretrained models like MERT for text understanding, ViT for image understanding, and ViViT for video understanding. By combining these models, the framework enables creative potential to be unlocked from diverse sources of inspiration.
To facilitate music generation, the M$^{2}$UGen framework utilizes AudioLDM 2 and MusicGen. These components provide the necessary tools and techniques for generating music based on the understanding obtained from LLMs. However, what truly sets M$^{2}$UGen apart is its ability to bridge multi-modal understanding and music generation through the integration of the LLaMA 2 model. This integration allows for a seamless translation of comprehended multi-modal inputs into musical outputs.
Furthermore, the MU-LLaMA model plays a crucial role in supporting the training of the M$^{2}$UGen framework. By generating extensive datasets that facilitate text/image/video-to-music generation, MU-LLaMA enables the framework to learn and improve its music generation capabilities. This training process ensures that the M$^{2}$UGen framework achieves or surpasses the performance of the current state-of-the-art models.
In the wider field of multimedia information systems, the M$^{2}$UGen framework represents a significant advancement. Its ability to comprehend and generate music across different modalities opens up new possibilities for creating immersive multimedia experiences. By combining the power of LLMs with various pretrained models and techniques, the framework demonstrates the potential for pushing the boundaries of what is possible in animations, artificial reality, augmented reality, and virtual realities.
In conclusion, the M$^{2}$UGen framework serves as a pivotal contribution to research leveraging large language models. Its integration of multi-modal understanding and music generation showcases the synergistic potential of combining different modalities. As this field continues to evolve and mature, we can expect further advancements in the realm of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.