cs.AI updates on arXiv.org 10月21日 12:13
多模态音乐生成系统研究
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本文提出一种融合生理传感、基于LLM的推理和可控音频合成的个性化音乐生成系统,通过雷达传感器非侵入性捕捉生理信号,结合环境状态进行音乐特征推断,以实现文化扎根和自适应音乐交互。

arXiv:2510.15895v1 Announce Type: cross Abstract: We present a multimodal system for personalized music generation that integrates physiological sensing, LLM-based reasoning, and controllable audio synthesis. A millimeter-wave radar sensor non-invasively captures heart rate and respiration rate. These physiological signals, combined with environmental state, are interpreted by a reasoning agent to infer symbolic musical descriptors, such as tempo, mood intensity, and traditional Chinese pentatonic modes, which are then expressed as structured prompts to guide a diffusion-based audio model in synthesizing expressive melodies. The system emphasizes cultural grounding through tonal embeddings and enables adaptive, embodied music interaction. To evaluate the system, we adopt a research-creation methodology combining case studies, expert feedback, and targeted control experiments. Results show that physiological variations can modulate musical features in meaningful ways, and tonal conditioning enhances alignment with intended modal characteristics. Expert users reported that the system affords intuitive, culturally resonant musical responses and highlighted its potential for therapeutic and interactive applications. This work demonstrates a novel bio-musical feedback loop linking radar-based sensing, prompt reasoning, and generative audio modeling.

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多模态音乐生成 生理传感 LLM推理 音频合成
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