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2025
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| Online Access: | https://doi.org/10.5281/zenodo.15839047 |
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| author | Madaghiele, Vincenzo Fasciani, Stefano |
| author_facet | Madaghiele, Vincenzo Fasciani, Stefano |
| contents | <p>This repository contains the video example for the paper:</p> <p>Vincenzo Madaghiele, Stefano Fasciani. "A listening agent for live control of synthesis parameters using reinforcement learning". In <em>Proceedings of AI and Music Creativity Conference (AIMC) 2024</em>, 9-11 September 2024, Oxford (UK).</p> <p><strong>Abstract:</strong></p> <p>This paper presents a novel approach for developing customized autonomous agents for musical improvisation. The model we propose employs reinforcement learning to adaptively control the parameters of a sound synthesizer in response to live audio from a musician. The agent is trained on a corpus of audio files that exemplify the musician’s instrument and stylistic repertoire. During training, the agent listens and learns to imitate the incoming sound according to a set of perceptual descriptors by continuously adjusting the parameters of the synthesizer it controls. To achieve this objective, the agent learns specific strategies that are characteristic of its autonomous behavior in a live interaction. In the paper, we provide a detailed description of the design and implementation of the model for the agent and discuss its application in three selected scenarios. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15839047 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A listening agent for live control of synthesis parameters using reinforcement learning Madaghiele, Vincenzo Fasciani, Stefano <p>This repository contains the video example for the paper:</p> <p>Vincenzo Madaghiele, Stefano Fasciani. "A listening agent for live control of synthesis parameters using reinforcement learning". In <em>Proceedings of AI and Music Creativity Conference (AIMC) 2024</em>, 9-11 September 2024, Oxford (UK).</p> <p><strong>Abstract:</strong></p> <p>This paper presents a novel approach for developing customized autonomous agents for musical improvisation. The model we propose employs reinforcement learning to adaptively control the parameters of a sound synthesizer in response to live audio from a musician. The agent is trained on a corpus of audio files that exemplify the musician’s instrument and stylistic repertoire. During training, the agent listens and learns to imitate the incoming sound according to a set of perceptual descriptors by continuously adjusting the parameters of the synthesizer it controls. To achieve this objective, the agent learns specific strategies that are characteristic of its autonomous behavior in a live interaction. In the paper, we provide a detailed description of the design and implementation of the model for the agent and discuss its application in three selected scenarios. </p> |
| title | A listening agent for live control of synthesis parameters using reinforcement learning |
| url | https://doi.org/10.5281/zenodo.15839047 |