Abstract
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.
Abstract (translated)
在创作过程中，选择合适的单乐器音乐序列并分配其轨道角色是一个不可或缺的任务。然而，手动确定许多音乐样本的轨道角色可能需要花费大量的时间和精力。本研究介绍了一种用于自动预测单乐器音乐序列轨道角色的深度学习模型。我们的评估结果显示，在符号域中的预测准确率为87%，在音频域中的预测准确率为84%。所提出的轨道角色预测方法具有很大的潜力，将在未来的AI音乐生成和分析应用中发挥重要作用。
URL
https://arxiv.org/abs/2404.13286
