{"type":"ScholarlyArticle","title":"SynthRL: Cross-domain Synthesizer Sound Matching via Reinforcement Learning","authors":[{"name":"Wonchul Shin","slug":"wonchul-shin","given":"Wonchul","family":"Shin","affiliation":""},{"name":"Kyogu Lee","slug":"kyogu-lee","given":"Kyogu","family":"Lee","affiliation":""}],"abstract":"Generalization of synthesizer sound matching to external instrument sounds is highly challenging due to the non-differentiability of sound synthesis process which prohibits the use of out-of-domain sounds for training with synthesis parameter loss. We propose SynthRL, a novel reinforcement learning (RL)-based approach for cross-domain synthesizer sound matching. By incorporating sound similarity into the reward function, SynthRL effectively optimizes synthesis parameters without ground-truth labels, allowing fine-tuning on out-of-domain sounds. Furthermore, we introduce a transformer-based model architecture and reward-based prioritized experience replay to enhance RL training efficiency, considering the unique characteristics of the task. Experimental results demonstrate that SynthRL outperforms state-of-the-art methods on both in-domain and out-of-domain tasks. Further experimental analysis validates the effectiveness of our reward design, showing a strong correlation with human perception of sound similarity.","doi":"10.24963/ijcai.2025/1129","url":"https://ijcai.org/proceedings/2025/1129","pdf":"https://ijcai.org/proceedings/2025/1129.pdf","markdown":"https://ijcai.org/proceedings/2025/1129.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1129","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":1129,"pages":{"first":10162,"last":10170},"keywords":["Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Music and sound","Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}