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Analyzable Chain-of-Musical-Thought Prompting for High-Fidelity Music Generation

Max W. Y. Lam, Yijin Xing, Weiya You, Jingcheng Wu, Zongyu Yin, Fuqiang Jiang, Hangyu Liu, Feng Liu, Xingda Li, Wei-Tsung Lu, Hanyu Chen, Tong Feng, Tianwei Zhao, Chien-Hung Liu, Xuchen Song, Yang Li, Yahui Zhou

TL;DR

This work addresses the gap between autoregressive music generation and the human creative process by introducing MusiCoT, an analyzable chain-of-musical-thought prompting framework. MusiCoT leverages CLAP embeddings as intermediate, continuous 'musical thoughts' and a residual vector quantization scheme to produce coarse-to-fine tokens that guide a diffusion-based acoustic model within the MeLoDy framework. It introduces dual-sampling strategies—Dual-Temperature Sampling and Dual-Scale Classifier-Free Guidance—to improve generation quality without increasing latency, and enables music referencing and structural analysis. Experiments show that MusiCoT improves subjective and objective metrics, achieving high fidelity and offering practical benefits for long-context, reference-enabled music prompting.

Abstract

Autoregressive (AR) models have demonstrated impressive capabilities in generating high-fidelity music. However, the conventional next-token prediction paradigm in AR models does not align with the human creative process in music composition, potentially compromising the musicality of generated samples. To overcome this limitation, we introduce MusiCoT, a novel chain-of-thought (CoT) prompting technique tailored for music generation. MusiCoT empowers the AR model to first outline an overall music structure before generating audio tokens, thereby enhancing the coherence and creativity of the resulting compositions. By leveraging the contrastive language-audio pretraining (CLAP) model, we establish a chain of "musical thoughts", making MusiCoT scalable and independent of human-labeled data, in contrast to conventional CoT methods. Moreover, MusiCoT allows for in-depth analysis of music structure, such as instrumental arrangements, and supports music referencing -- accepting variable-length audio inputs as optional style references. This innovative approach effectively addresses copying issues, positioning MusiCoT as a vital practical method for music prompting. Our experimental results indicate that MusiCoT consistently achieves superior performance across both objective and subjective metrics, producing music quality that rivals state-of-the-art generation models. Our samples are available at https://MusiCoT.github.io/.

Analyzable Chain-of-Musical-Thought Prompting for High-Fidelity Music Generation

TL;DR

This work addresses the gap between autoregressive music generation and the human creative process by introducing MusiCoT, an analyzable chain-of-musical-thought prompting framework. MusiCoT leverages CLAP embeddings as intermediate, continuous 'musical thoughts' and a residual vector quantization scheme to produce coarse-to-fine tokens that guide a diffusion-based acoustic model within the MeLoDy framework. It introduces dual-sampling strategies—Dual-Temperature Sampling and Dual-Scale Classifier-Free Guidance—to improve generation quality without increasing latency, and enables music referencing and structural analysis. Experiments show that MusiCoT improves subjective and objective metrics, achieving high fidelity and offering practical benefits for long-context, reference-enabled music prompting.

Abstract

Autoregressive (AR) models have demonstrated impressive capabilities in generating high-fidelity music. However, the conventional next-token prediction paradigm in AR models does not align with the human creative process in music composition, potentially compromising the musicality of generated samples. To overcome this limitation, we introduce MusiCoT, a novel chain-of-thought (CoT) prompting technique tailored for music generation. MusiCoT empowers the AR model to first outline an overall music structure before generating audio tokens, thereby enhancing the coherence and creativity of the resulting compositions. By leveraging the contrastive language-audio pretraining (CLAP) model, we establish a chain of "musical thoughts", making MusiCoT scalable and independent of human-labeled data, in contrast to conventional CoT methods. Moreover, MusiCoT allows for in-depth analysis of music structure, such as instrumental arrangements, and supports music referencing -- accepting variable-length audio inputs as optional style references. This innovative approach effectively addresses copying issues, positioning MusiCoT as a vital practical method for music prompting. Our experimental results indicate that MusiCoT consistently achieves superior performance across both objective and subjective metrics, producing music quality that rivals state-of-the-art generation models. Our samples are available at https://MusiCoT.github.io/.

Paper Structure

This paper contains 24 sections, 6 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Music Generation
  4. Chain-of-Thought Prompting
  5. Music Generation Framework
  6. Semantic Modeling with a Semantic Language Model
  7. Acoustic Modeling with a Diffusion Model
  8. Conditional Music Generation with Lyrics and Text Prompts
  9. MusiCoT: Analyzable Chain-of-Musical-Thought Prompting
  10. Viewing CLAP Audio Embeddings as Analyzable Musical Thoughts
  11. Predicting Coarse-to-Fine Flattened RVQ for a Stabler MusiCoT Training
  12. Dual-Sampling Strategy for MusiCoT
  13. Dual-Temperature Sampling
  14. Dual-Scale Classifier-Free Guidance
  15. Experiments
  16. ...and 9 more sections

Figures (3)

  • Figure 1: This illustration showcases the MusiCoT reasoning process for music generation, focusing on instrumental arrangement. The arrows are color-coded to indicate the intensity of each instrument: darker colors represent higher intensity, while lighter shades signify lower intensity.
  • Figure 2: The diagram illustrating the computation of flattened CLAP RVQ tokens given an audio.
  • Figure 3: The diagram presenting the token arrangement in MusiCoT-based autoregressive model and the structural analyzability obtained from the CLAP RVQ token prediction.