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一种基于变压器的方法,用于细粒度和粗粒度的分类和生成MIDI歌曲和声带
Simone Angioni1, Nathan Lincoln-DeCusatis2, Andrea Ibba1
1Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Sardegna, Italy.
PeerJ. Computer science
|July 6, 2023
概括
变压器是一种人工智能,可以有效地分类和生成音乐. 这种人工智能方法在组织音乐风格和从各种数据集 (如视频游戏音乐和古典作曲) 中创建新作品方面超过了现有的方法.
科学领域:
- 人工智能的人工智能
- 音乐心理学 音乐心理学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 音乐的分类和生成是复杂的,因为音乐的主观性质和类型的不断变化的定义.
- 人工智能,特别是深度学习,为分析和创建音乐提供了先进的工具.
- 自我注意网络 (变压器) 在包括声音在内的各种数据领域显示出前景.
研究的目的:
- 评估变压器在音乐分类和生成任务中的有效性.
- 在细粒度 (作曲家/类型) 和粗粒度 (类型) 层面对音乐进行分类时分析变压器的性能.
- 用人类和自动指标来评估音乐生成质量.
主要方法:
- 利用变压器模型进行音乐分类和生成.
- 在各种MIDI数据集上训练模型:任天堂娱乐系统 (NES) 视频游戏音乐,古典音乐和摇滚歌曲.
- 在数据集内进行细粒度分类,并在组合数据集中进行粗粒度分类.
主要成果:
- 基于变压器的方法在音乐分类的其他深度学习和机器学习方法相比表现出了更好的表现.
- 在不同细粒度 (细粒度和粗粒度) 上分析了分类准确性.
- 生成的音乐样本使用人类判断和自动指标 (如局部对齐) 进行了评估.
结论:
- 变压器对于音乐分类和生成都非常有效.
- 拟议的AI方法为组织和创作音乐提供了一个强大的工具,优于现有的方法.
- 这项研究强调了先进的人工智能在推进音乐信息检索和创意应用方面的潜力.
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