塔里恩:一个新的变压器与一个门的重复单元为风格化的音乐世代
Yumei Zhang1,2,3, Yulin Zhou1,2, Xiaojiao Lv1,2
1School of Computer Science, Shaanxi Normal University, Xi'an 710062, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究介绍了TARREAN,这是一种用于音乐生成的新型AI模型,可以增强时间连贯性并降低计算成本. TARREAN显著提高了人工智能生成的音乐质量,更好地与人类的偏好保持一致.
科学领域:
- 人工智能的人工智能
- 音乐信息检索 音乐信息检索
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 人工智能驱动的音乐生成是一个不断增长的研究领域.
- 现有的基于变压器的模型面临着连贯性和计算成本的挑战.
- 时间依赖和因果结构对于现实的音乐序列至关重要.
研究的目的:
- 提出一种基于变压器的新型模型,TARREAN,用于改进AI音乐生成.
- 解决当前音乐生成方法中的连贯性和高计算成本问题.
- 为了提高人工智能生成的音乐的质量和人与人相似.
主要方法:
- 开发了TARREAN,这是一个集成门反复单位 (GRU) 和根平均平方规范限制 (RMS Norm) 的变压器模型.
- 员工在培训期间掩盖了多头注意力,以保持因果结构.
- 利用复合词方法将音乐序列编码成离散的符号-事件组合.
主要成果:
- TARREAN在生成的音乐序列中显示出更好的时间连贯性.
- 与变压器-XL + REMI (3.79) 相比,该模型获得了更高的主观满意度得分 (4.34).
- 客观评估显示,与传统方法相比,时间连贯性有15%的改善,计算成本降低.
结论:
- TARREAN有效地减轻了不连续性问题,并提高了音乐生成质量.
- 拟议的模型为AI音乐生成提供了显著的连贯性和效率的改进.
- TARREAN代表了创造更符合人类的AI生成音乐的有希望的进步.
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