MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912656349200384 |
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| author | Chae, Yunkee Lee, Kyogu |
| author_facet | Chae, Yunkee Lee, Kyogu |
| contents | 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. Audio samples are available at our project page: https://yoongi43.github.io/MGELDM_Samples/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction Chae, Yunkee Lee, Kyogu Sound Machine Learning Audio and Speech Processing 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. Audio samples are available at our project page: https://yoongi43.github.io/MGELDM_Samples/. |
| title | MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.23305 |