MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction

Yunkee Chae, Kyogu Lee

Keywords: Music Generation, Source Extraction, Inpainting

TL;DR: We propose unified latent diffusion framework for simultaneous music generation, source imputation, and query-driven arbitrary source extraction.

Abstract: We present MGE-LDM, a unified latent diffusion framework for simultaneous music generation, source imputation, and query-driven source separation. Unlike prior approaches constrained to fixed instrument classes, MGE-LDM learns a joint distribution over full mixtures, submixtures, and individual stems within a single compact latent diffusion model. At inference, MGE-LDM enables (1) complete mixture generation, (2) partial generation (i.e., source imputation), and (3) text-conditioned extraction of arbitrary sources. By formulating both separation and imputation as conditional inpainting tasks in the latent space, our approach supports flexible, class-agnostic manipulation of arbitrary instrument sources. Notably, MGE-LDM can be trained jointly across heterogeneous multi-track datasets (e.g., Slakh2100, MUSDB18, MoisesDB) without relying on predefined instrument categories.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 22117