F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913691446804480 |
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| author | Agarwal, Manvi Wang, Changhong Richard, Gael |
| author_facet | Agarwal, Manvi Wang, Changhong Richard, Gael |
| contents | While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to leverage information about musical structure in Transformers is inserting such knowledge into the positional encoding (PE) module. However, Transformers carry a quadratic cost in sequence length. In this paper, we propose F-StrIPE, a structure-informed PE scheme that works in linear complexity. Using existing kernel approximation techniques based on random features, we show that F-StrIPE is a generalization of Stochastic Positional Encoding (SPE). We illustrate the empirical merits of F-StrIPE using melody harmonization for symbolic music. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation Agarwal, Manvi Wang, Changhong Richard, Gael Sound Artificial Intelligence Machine Learning Audio and Speech Processing While music remains a challenging domain for generative models like Transformers, recent progress has been made by exploiting suitable musically-informed priors. One technique to leverage information about musical structure in Transformers is inserting such knowledge into the positional encoding (PE) module. However, Transformers carry a quadratic cost in sequence length. In this paper, we propose F-StrIPE, a structure-informed PE scheme that works in linear complexity. Using existing kernel approximation techniques based on random features, we show that F-StrIPE is a generalization of Stochastic Positional Encoding (SPE). We illustrate the empirical merits of F-StrIPE using melody harmonization for symbolic music. |
| title | F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.10491 |