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.

翻译：本研究提出了一种基于扩散架构的音色转换模型，旨在将各类乐器演奏的音乐精确转换为钢琴版本。该模型采用音高编码器与响度编码器提取音乐的音高与响度特征，作为扩散模型解码器的条件输入，从而生成高质量的钢琴音色。案例分析结果表明，该模型在音高准确性与音色相似度方面表现优异，能够在不同音乐风格（古典、爵士、流行）与长度（从短片段到完整乐曲）下保持稳定的转换效果。特别是在处理快速变化的音符与复杂音乐结构时，模型仍能保持较高的音质与准确度，展现出良好的泛化能力。此外，该模型具备实时音乐转换的潜力，适用于现场演出与数字音乐创作工具。未来研究将集中于增强对响度动态变化的处理能力，并融入更多音乐特征（如音色变化与节奏复杂度），以提升模型的适应性与表现力。我们计划探索该模型在其他音色转换任务中的应用潜力，例如人声转乐器声或与MIDI数字钢琴的集成，进一步拓展基于扩散模型的音色转换技术在音乐生成领域的应用范围。