Genre Controlled Music Generation via Activation Steering
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918524239216640 |
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| author | Narashiman, Swathi Mathur, Pranay Panda, Dipanshu Joe, Jayden Koshy R, Harshith M Veerakumar, Anish Krishna, Aniruddh A, Keerthiharan |
| author_facet | Narashiman, Swathi Mathur, Pranay Panda, Dipanshu Joe, Jayden Koshy R, Harshith M Veerakumar, Anish Krishna, Aniruddh A, Keerthiharan |
| contents | Computational Music Generation is evolving towards non-conventional styles, demanding methods that enable precise and controllable blending of diverse music elements. In this work, we present a method for fine grained control using inference-time interventions on an autoregressive generative transformer, MusicGen. Through our approach, we achieve genre control by steering the residual stream using weights of a linear probe on it. By framing activation steering as a human-controllable interaction, our work highlights how interpretable model behaviors can empower in co-creative music generation.Audio samples demonstrating our method are available on our demo page. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genre Controlled Music Generation via Activation Steering Narashiman, Swathi Mathur, Pranay Panda, Dipanshu Joe, Jayden Koshy R, Harshith M Veerakumar, Anish Krishna, Aniruddh A, Keerthiharan Sound Artificial Intelligence Audio and Speech Processing Computational Music Generation is evolving towards non-conventional styles, demanding methods that enable precise and controllable blending of diverse music elements. In this work, we present a method for fine grained control using inference-time interventions on an autoregressive generative transformer, MusicGen. Through our approach, we achieve genre control by steering the residual stream using weights of a linear probe on it. By framing activation steering as a human-controllable interaction, our work highlights how interpretable model behaviors can empower in co-creative music generation.Audio samples demonstrating our method are available on our demo page. |
| title | Genre Controlled Music Generation via Activation Steering |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.10225 |