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M$^{2}$UGen: Multi-modal Music Understanding and Generation with the Power of Large Language Models (2311.11255v5)

Published 19 Nov 2023 in cs.SD, cs.MM, and eess.AS

Abstract: The current landscape of research leveraging 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.

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Authors (5)
  1. Atin Sakkeer Hussain (5 papers)
  2. Shansong Liu (19 papers)
  3. Chenshuo Sun (5 papers)
  4. Ying Shan (252 papers)
  5. Qilong Wu (25 papers)
Citations (13)
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Summary

An Analysis of M2^2UGen: Multi-modal Music Understanding and Generation with LLMs

The paper "M2^2UGen: Multi-modal Music Understanding and Generation with the Power of LLMs" presents a sophisticated framework for integrating LLMs into the field of multi-modal music comprehension and creation. This research capitalizes on the burgeoning capabilities of LLMs by extending their application to the understanding and generation of music across diverse modalities, accomplishing this integration via a cohesive and structured model design.

The M2^2UGen framework is pioneering in its incorporation of multiple modality inputs—text, image, and video—through the use of pre-trained encoders such as MERT for music, ViT for images, and ViViT for video. These encoders transform inputs into feature embeddings, which are processed by specifically designed adapters for comprehension within the LLaMA 2 model architecture. The framework is notably adept at handling music generation by utilizing music decoders like AudioLDM 2 and MusicGen, and forges a connection between multi-modal understanding and music generation through intricate model integrations.

An exceptional strength of this research is its capacity to concurrently address music understanding and multi-modal music production within a unified framework. Experimental evaluations highlight M2^2UGen's capability to either meet or surpass current state-of-the-art models across distinct tasks, such as music question answering and text/image/video-to-music generation. The integration of LLMs, manifested in this framework, underscores the dual-purpose adaptability in both enriching multimedia comprehension and facilitating complex content generation.

From a numerical standpoint, the paper provides robust evaluation metrics, including BLEU, METEOR, ROUGE, and BERT-Score for music understanding, complemented by FAD, KL, and CLAP scores for text-to-music and other modality-based generation assessments. These metrics assert M2^2UGen's position as a formidable construct in achieving superior or comparable effectiveness relative to established models.

The implications of this research traverse both theoretical and practical domains. Theoretically, it establishes a groundwork for expanding LLM functionalities into multi-modal domains beyond text. Practically, it opens pathways for implementing AI systems in creative fields, such as music composition, media content creation, and interactive entertainment, where understanding the nuance between modalities is indispensable.

Future developments in AI could further refine the delicate balance between understanding and generation tasks. Enhancing the model's fine-grained comprehension of the iterative subtleties in music understanding and generation remains a prospective area for research. Moreover, further corpus expansion beyond existing datasets like MusicQA and MusicCaps could aid in fortifying the model's proficiency in these domains.

In conclusion, the M2^2UGen framework signifies a notable progression in the synthesis of LLMs within multi-modal music understanding and generation, establishing itself as a versatile and high-performing tool in both academic research and applied technology spheres. The nuanced melding of modalities integrated with the generative strength of LLMs sets a new benchmark in the ongoing fusion of AI with creative processes.

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