Track Role Prediction of Single-Instrumental Sequences
<|reference_start|>Track Role Prediction of Single-Instrumental Sequences: In the composition process, selecting appropriate single-instrumental music sequences and assigning their track-role is an indispensable task. However, manually determining the track-role for a myriad of music samples can be time-consuming and labor-intensive. This study introduces a deep learning model designed to automatically predict the track-role of single-instrumental music sequences. Our evaluations show a prediction accuracy of 87% in the symbolic domain and 84% in the audio domain. The proposed track-role prediction methods hold promise for future applications in AI music generation and analysis.<|reference_end|>
@article{han2024track,
    title={Track Role Prediction of Single-Instrumental Sequences},
    author={Changheon Han, Suhyun Lee, and Minsam Ko},
    journal={arXiv preprint arXiv:2404.13286},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13286},
    primaryClass={cs.SD cs.IR eess.AS}
}