Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation
As symbolic music generation evolves, research interest is shifting toward more controlled and steerable generative processes to support creative decisions. Previous methods focus on global conditioning or fine-grained control through input sequences but often limit flexibility for real-time interve...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10879291/ |
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| author | Rafik Hachana Bader Rasheed |
| author_facet | Rafik Hachana Bader Rasheed |
| author_sort | Rafik Hachana |
| collection | DOAJ |
| description | As symbolic music generation evolves, research interest is shifting toward more controlled and steerable generative processes to support creative decisions. Previous methods focus on global conditioning or fine-grained control through input sequences but often limit flexibility for real-time interventions and require modifications to the model’s architecture. We introduce a novel symbolic music generation framework by combining a Transformer encoder-decoder with probe models, which enable us to interpret the encoder hidden state using pre-defined non-differentiable musical features, and subsequently manipulate the hidden state to achieve a set of desired attributes in the generated music. This method allows fine-grained control over specific musical features without altering the underlying model architecture. Probes can be trained jointly with the generative model or applied post-training, enabling adaptable control without retraining the model. Our experiments demonstrate that this intervention effectively influences the model output without hindering the music quality. This approach enhances both the flexibility and interpretability of symbolic music generation, enabling better real-world applicability for music generation models. |
| format | Article |
| id | doaj-art-69ae3eafae244937b95fd2b49b058ea7 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-69ae3eafae244937b95fd2b49b058ea72025-08-20T03:11:58ZengIEEEIEEE Access2169-35362025-01-0113280592807010.1109/ACCESS.2025.354054310879291Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music GenerationRafik Hachana0https://orcid.org/0009-0003-0651-2445Bader Rasheed1https://orcid.org/0000-0003-3874-0883Research Center for Artificial Intelligence, Innopolis University, Innopolis, RussiaResearch Center for Artificial Intelligence, Innopolis University, Innopolis, RussiaAs symbolic music generation evolves, research interest is shifting toward more controlled and steerable generative processes to support creative decisions. Previous methods focus on global conditioning or fine-grained control through input sequences but often limit flexibility for real-time interventions and require modifications to the model’s architecture. We introduce a novel symbolic music generation framework by combining a Transformer encoder-decoder with probe models, which enable us to interpret the encoder hidden state using pre-defined non-differentiable musical features, and subsequently manipulate the hidden state to achieve a set of desired attributes in the generated music. This method allows fine-grained control over specific musical features without altering the underlying model architecture. Probes can be trained jointly with the generative model or applied post-training, enabling adaptable control without retraining the model. Our experiments demonstrate that this intervention effectively influences the model output without hindering the music quality. This approach enhances both the flexibility and interpretability of symbolic music generation, enabling better real-world applicability for music generation models.https://ieeexplore.ieee.org/document/10879291/Machine learningmusic generationsymbolic musicgenerative AIprobesconditional generative models |
| spellingShingle | Rafik Hachana Bader Rasheed Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation IEEE Access Machine learning music generation symbolic music generative AI probes conditional generative models |
| title | Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation |
| title_full | Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation |
| title_fullStr | Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation |
| title_full_unstemmed | Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation |
| title_short | Probe-Assisted Fine-Grained Control for Non-Differentiable Features in Symbolic Music Generation |
| title_sort | probe assisted fine grained control for non differentiable features in symbolic music generation |
| topic | Machine learning music generation symbolic music generative AI probes conditional generative models |
| url | https://ieeexplore.ieee.org/document/10879291/ |
| work_keys_str_mv | AT rafikhachana probeassistedfinegrainedcontrolfornondifferentiablefeaturesinsymbolicmusicgeneration AT baderrasheed probeassistedfinegrainedcontrolfornondifferentiablefeaturesinsymbolicmusicgeneration |