The content discusses the development of a novel positional encoding framework called StructurePE for music generation using Transformers. Three variants are explored, each focusing on different aspects of positional information. The study compares these variants with baselines from the literature and demonstrates improved melodic and structural consistency in the generated music. The experiments cover tasks like next-timestep prediction and accompaniment generation, showcasing the effectiveness of the proposed methods. Additionally, insights into input representation, positional encoding techniques, and evaluation metrics are provided to support the findings.
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