Mozart's Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models

• URL: http://arxiv.org/abs/2405.02801v3
• Date: Mon, 25 Nov 2024 08:32:13 GMT
• Title: Mozart's Touch: A Lightweight Multi-modal Music Generation Framework Based on Pre-Trained Large Models
• Authors: Jiajun Li, Tianze Xu, Xuesong Chen, Xinrui Yao, Shuchang Liu,
• Abstract summary: Mozart's Touch is a framework capable of generating music aligned with cross-modal inputs such as images, videos, and text. Unlike traditional end-to-end methods, Mozart's Touch uses LLMs to accurately interpret visual elements without requiring the training or fine-tuning of music generation models.
• Score: 9.311353871322325
• License:
• Abstract: In recent years, AI-Generated Content (AIGC) has witnessed rapid advancements, facilitating the creation of music, images, and other artistic forms across a wide range of industries. However, current models for image- and video-to-music synthesis struggle to capture the nuanced emotions and atmosphere conveyed by visual content. To fill this gap, we propose Mozart's Touch, a multi-modal music generation framework capable of generating music aligned with cross-modal inputs such as images, videos, and text. The framework consists of three key components: Multi-modal Captioning Module, Large Language Model (LLM) understanding \& Bridging Module, and Music Generation Module. Unlike traditional end-to-end methods, Mozart's Touch uses LLMs to accurately interpret visual elements without requiring the training or fine-tuning of music generation models, providing efficiency and transparency through clear, interpretable prompts. We also introduce the "LLM-Bridge" method to resolve the heterogeneous representation challenges between descriptive texts from different modalities. Through a series of objective and subjective evaluations, we demonstrate that Mozart's Touch outperforms current state-of-the-art models. Our code and examples are available at https://github.com/TiffanyBlews/MozartsTouch.

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