用扩散模型实现电吉他音色自然过渡，效果优于传统微调方法。
Guitar Tone Morphing by Diffusion-based Model
- 采用Music2Latent的球面插值法，简化音色变换流程。
- 在有限数据下仍生成更平滑自然的音色过渡效果。
- 适合音乐制作人和实时音频特效开发者使用。
在音乐信息检索领域，建模与转换乐器音色，特别是电吉他的音色，因其丰富的音色表现力和灵活的表达方式而受到越来越多关注。音色变形技术可实现不同吉他音色间的平滑过渡，为音乐人提供更多创作自由，探索新音效并个性化演奏。本研究探索基于学习的电吉他音色变形方法，首先通过LoRA微调提升模型在少量数据下的性能；此外，提出一种更简便的方法——Music2Latent的球面插值法，其效果显著优于复杂的微调方案。实验表明，所提架构生成的音色过渡更加平滑自然，具备实用性与高效性，适用于音乐制作及实时音频效果处理。
原文摘要 · Abstract (English)
In Music Information Retrieval (MIR), modeling and transforming the tone of musical instruments, particularly electric guitars, has gained increasing attention due to the richness of the instrument tone and the flexibility of expression. Tone morphing enables smooth transitions between different guitar sounds, giving musicians greater freedom to explore new textures and personalize their performances. This study explores learning-based approaches for guitar tone morphing, beginning with LoRA fine-tuning to improve the model performance on limited data. Moreover, we introduce a simpler method, named spherical interpolation using Music2Latent. It yields significantly better results than the more complex fine-tuning approach. Experiments show that the proposed architecture generates smoother and more natural tone transitions, making it a practical and efficient tool for music production and real-time audio effects.
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