Guitar Tone Morphing by Diffusion-Based Model
Journal
2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025
Start Page
329
End Page
333
ISBN (of the container)
979-833157206-8
Date Issued
2025-11-28
Author(s)
Abstract
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 learningbased 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.
Event(s)
17th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025
Publisher
IEEE
Type
conference paper