Structure-informed Positional Encoding for Music Generation
<|reference_start|>Structure-informed Positional Encoding for Music Generation: 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.<|reference_end|>
@article{agarwal2024structure-informed,
    title={Structure-informed Positional Encoding for Music Generation},
    author={Manvi Agarwal (S2A, IDS), Changhong Wang (S2A, IDS), Ga"el Richard
  (S2A, IDS)},
    journal={IEEE International Conference on Acoustics, Speech and Signal
  Processing (ICASSP), Apr 2024, Seoul, South Korea},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13301},
    primaryClass={cs.SD cs.AI eess.AS}
}