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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10879291/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |