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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

TL;DR

The paper addresses the lack of objective feedback for bedroom producers by introducing AI TrackMate, an LLM-driven music chatbot that combines audio analysis with natural-language critique. It couples a Music Analysis Module, an LLM-Readable Music Report, and Music Production-Oriented Feedback Instructions, leveraging Graph-of-Thought prompting to bridge technical analysis and musical impact. The system is plug-and-play and training-free, compatible with multiple LLMs, and is validated through an interactive web interface and a pilot with a real producer. Findings suggest potential to support on-demand, objective self-assessment and skill development in independent music production, while highlighting avenues for broader genre coverage and real-time integration.

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.

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

TL;DR

The paper addresses the lack of objective feedback for bedroom producers by introducing AI TrackMate, an LLM-driven music chatbot that combines audio analysis with natural-language critique. It couples a Music Analysis Module, an LLM-Readable Music Report, and Music Production-Oriented Feedback Instructions, leveraging Graph-of-Thought prompting to bridge technical analysis and musical impact. The system is plug-and-play and training-free, compatible with multiple LLMs, and is validated through an interactive web interface and a pilot with a real producer. Findings suggest potential to support on-demand, objective self-assessment and skill development in independent music production, while highlighting avenues for broader genre coverage and real-time integration.

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.

Paper Structure

This paper contains 13 sections, 3 figures.

Table of Contents

  1. Introduction
  2. System
  3. Music Analysis Module
  4. LLM Readable Music Report
  5. Music Production-Oriented Feedback Instruction
  6. Foundational Prompt Template.
  7. Track Improvement Suggestion
  8. Web-based UI
  9. Pilot Study
  10. Producer Profiles
  11. AI-Producer Dialogues
  12. Producer Opinion on AI Feedback
  13. Conclusion & Discussion

Figures (3)

  • Figure 1: The system comprises three layers: (1) User Interface for audio upload, query input, and feedback reception; (2) Data Processing for handling raw audio and text; and (3) AI Analysis, featuring a Music Analysis Module that transforms raw audio into LLM-readable report, and an LLM that processes these reports along with user queries. Guided by music production-oriented feedback instructions, the LLM generates insights comparable to those of a music producer.
  • Figure 2: Graph of Thoughts (GoT) approach applies to analyze dominant instruments' impact on a track's emotional tone. Our workflow progresses through generate (T1, T3), aggregate (T2, T4b), and refine (T4a, T5) transformations, adhering to the original GoT framework. We represent analytical steps as nodes (N1-N11), demonstrating how GoT decomposes complex musical analysis into interconnected nodes. This structure elucidates how instrument interactions contribute to the track's overall emotional impact. Through this application of GoT, we enable a systematic exploration of the relationship between instrumental composition and emotional resonance in music.
  • Figure 3: The user interface consists of: (1) Audio input components for file upload or YouTube link input. (2) A chat window displaying the LLM's scoring and suggestions. (3) A text input for user questions and LLM responses.