AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!"
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916514692595712 |
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| author | Jiang, Yi-Lin Hsiung, Chia-Ho Yeh, Yen-Tung Chen, Lu-Rong Chen, Bo-Yu |
| author_facet | Jiang, Yi-Lin Hsiung, Chia-Ho Yeh, Yen-Tung Chen, Lu-Rong Chen, Bo-Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06617 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!" Jiang, Yi-Lin Hsiung, Chia-Ho Yeh, Yen-Tung Chen, Lu-Rong Chen, Bo-Yu Sound Human-Computer Interaction Machine Learning Multimedia Audio and Speech Processing 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. |
| title | AI TrackMate: Finally, Someone Who Will Give Your Music More Than Just "Sounds Great!" |
| topic | Sound Human-Computer Interaction Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.06617 |