arXiv:2412.06617cs.SDcs.HC2024-12中稿 · NeurIPS被引 1

AI TrackMate让独立音乐人获得专业级的制作反馈

AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!"

  • 用大模型+音频分析结合,给出具体制作建议
  • 无需训练,兼容多种大模型,可实时反馈
  • 适合独立音乐人自我评估与技能提升

随着'卧室制作人'的兴起,音乐创作门槛降低,但自我评估愈发困难。为此,我们提出AI TrackMate,一个基于大语言模型的音乐聊天机器人,可对音乐作品提供建设性反馈。通过融合大模型的音乐知识与直接音频轨道分析,该系统区别于纯文本方法,提供针对性制作建议。其框架包含音乐分析模块、大模型可读的音乐报告以及面向制作的反馈指令,实现即插即用、无需训练,且可适配未来模型升级。我们通过交互式网页界面展示其功能,并在一位音乐制作人的试点研究中验证效果。AI TrackMate将AI能力与独立制作者需求结合,提供按需分析反馈,有望支持创作过程与技能成长,回应了独立音乐制作领域日益增长的客观自评工具需求。

原文摘要 · Abstract (English)

The rise of "bedroom producers" has democratized music creation, while challenging producers to objectively evaluate their work. To address this, we present AI TrackMate, an LLM-based music chatbot designed to provide constructive feedback on music productions. By combining LLMs' inherent musical knowledge with direct audio track analysis, AI TrackMate offers production-specific insights, distinguishing it from text-only approaches. Our framework integrates a Music Analysis Module, an LLM-Readable Music Report, and Music Production-Oriented Feedback Instruction, creating a plug-and-play, training-free system compatible with various LLMs and adaptable to future advancements. We demonstrate AI TrackMate's capabilities through an interactive web interface and present findings from a pilot study with a music producer. By bridging AI capabilities with the needs of independent producers, AI TrackMate offers on-demand analytical feedback, potentially supporting the creative process and skill development in music production. This system addresses the growing demand for objective self-assessment tools in the evolving landscape of independent music production.

音乐生成LLM应用音频分析

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