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

Yi-Lin Jiang, Chia-Ho Hsiung, Yen-Tung Yeh, Lu-Rong Chen, Bo-Yu Chen
12/9/2024
cs.SDcs.HCcs.LG

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Abstract

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.

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Cite this paper

@article{jiang2024trackmate,
  title  = {AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!"},
  author = {Yi-Lin Jiang and Chia-Ho Hsiung and Yen-Tung Yeh and Lu-Rong Chen and Bo-Yu Chen},
  year   = {2024},
  eprint = {2412.06617},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.06617v1}
}

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