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arXiv · 2402.13301

Structure-informed Positional Encoding for Music Generation

Manvi Agarwal·Changhong Wang·Gaël Richard
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Abstract

Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generation with Transformers. We design three variants in terms of absolute, relative and non-stationary positional information. We comprehensively test them on two symbolic music generation tasks: next-timestep prediction and accompaniment generation. As a comparison, we choose multiple baselines from the literature and demonstrate the merits of our methods using several musically-motivated evaluation metrics. In particular, our methods improve the melodic and structural consistency of the generated pieces.

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Manvi Agarwal, Changhong Wang, Gaël Richard. 2024-02-20. Structure-informed Positional Encoding for Music Generation. https://arxiv.org/abs/2402.13301

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