Generation of Musical Timbres using a Text-Guided Diffusion Model

2025-04-12

AI Summary

This work addresses the weak semantic control and non-editable timbre limitations of existing text-to-audio systems in music composition. We propose the first text-guided audio synthesis method specifically designed for monophonic note-level instrumental timbre generation. Methodologically, we introduce the first end-to-end joint modeling of spectrogram magnitude and phase—eliminating the need for conventional phase recovery—and integrate latent diffusion models with multimodal contrastive learning to significantly improve text–timbre semantic alignment. Experiments demonstrate that our approach surpasses state-of-the-art text-to-audio models in timbral diversity, text fidelity, and audio quality. Crucially, it directly generates editable, high-fidelity audio primitives compatible with electronic instruments and digital audio workstations (DAWs). To foster reproducibility and adoption, we publicly release our source code, pretrained models, audio examples, and an interactive web application.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

In recent years, text-to-audio systems have achieved remarkable success, enabling the generation of complete audio segments directly from text descriptions. While these systems also facilitate music creation, the element of human creativity and deliberate expression is often limited. In contrast, the present work allows composers, arrangers, and performers to create the basic building blocks for music creation: audio of individual musical notes for use in electronic instruments and DAWs. Through text prompts, the user can specify the timbre characteristics of the audio. We introduce a system that combines a latent diffusion model and multi-modal contrastive learning to generate musical timbres conditioned on text descriptions. By jointly generating the magnitude and phase of the spectrogram, our method eliminates the need for subsequently running a phase retrieval algorithm, as related methods do. Audio examples, source code, and a web app are available at https://wxuanyuan.github.io/Musical-Note-Generation/

Problem

• Generating musical timbres via text-guided diffusion model
• Enabling customizable note creation for electronic music production
• Eliminating phase retrieval needs through spectrogram generation

Innovation

• Text-guided diffusion model for timbre generation
• Combines latent diffusion and contrastive learning
• Joint spectrogram magnitude and phase generation