Structure-informed Positional Encoding for Music Generation

• URL: http://arxiv.org/abs/2402.13301v2
• Date: Wed, 28 Feb 2024 12:37:34 GMT
• Title: Structure-informed Positional Encoding for Music Generation
• Authors: Manvi Agarwal (S2A, IDS), Changhong Wang (S2A, IDS), Ga\"el Richard (S2A, IDS)
• Abstract summary: We propose a structure-informed positional encoding framework for music generation with Transformers. We test them on two symbolic music generation tasks: next-timestep prediction and accompaniment generation. Our methods improve the melodic and structural consistency of the generated pieces.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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