Research on Piano Timbre Transformation System Based on Diffusion Model

2026-01-14

AI Summary

This work proposes a diffusion-based approach for high-fidelity timbre transfer that accurately converts multi-instrumental music into piano timbre while preserving pitch accuracy and timbral similarity. The method employs dedicated pitch and loudness encoders to extract conditioning features, which guide a conditional diffusion decoder to generate high-quality audio. As the first study to apply diffusion models to timbre conversion from diverse musical styles and variable-length segments to piano sound, the system demonstrates stable and high-fidelity performance across complex pieces spanning classical, jazz, and pop genres. It effectively handles rapid note sequences and intricate musical structures, exhibiting strong generalization capabilities and promising potential for real-time applications.

Technology Category

Computer Vision: Diffusion Models for VisionMachine Learning: Deep Generative Models & AutoencodersNatural Language Processing: Generation

Application Category

Web Mining and Content Analysis: Large pretrained models with web dataEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsSearch and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

We propose a timbre conversion model based on the Diffusion architecture de-signed to precisely translate music played by various instruments into piano ver-sions. The model employs a Pitch Encoder and Loudness Encoder to extract pitch and loudness features of the music, which serve as conditional inputs to the Dif-fusion Model's decoder, generating high-quality piano timbres. Case analysis re-sults show that the model performs excellently in terms of pitch accuracy and timbral similarity, maintaining stable conversion across different musical styles (classical, jazz, pop) and lengths (from short clips to full pieces). Particularly, the model maintains high sound quality and accuracy even when dealing with rapidly changing notes and complex musical structures, demonstrating good generaliza-tion capability. Additionally, the model has the potential for real-time musical conversion and is suitable for live performances and digital music creation tools. Future research will focus on enhancing the handling of loudness dynamics and incorporating additional musical features (such as timbral variations and rhythmic complexity) to improve the model's adaptability and expressiveness. We plan to explore the model's application potential in other timbre conversion tasks, such as converting vocals to instrumental sounds or integration with MIDI digital pianos, further expanding the application scope of the Diffusion-based timbre conversion model in the field of music generation.

Problem

• timbre transformation
• piano timbre
• music conversion
• diffusion model
• cross-instrument synthesis

Innovation

• Diffusion Model
• Timbre Conversion
• Pitch Encoder
• Loudness Encoder
• Real-time Music Generation