辅助自我监督的度量学习,以音乐相似性为基础的检索和自动标记
Taketo Akama1, Hiroaki Kitano1, Katsuhiro Takematsu2
1Sony Computer Science Laboratories, Inc, Tokyo, Japan.
PloS one
|November 30, 2023
概括
这项研究引入了一种新的自我监督度量学习方法,用于音乐信息检索,增强基于相似性的检索和自动标记性能. 该方法有效地从音乐音频数据中学习,即使在有限的人类注释标签的情况下也可以改善结果.
科学领域:
- 音乐信息检索 音乐信息检索
- 机器学习 机器学习
- 音频信号处理 音频信号处理
背景情况:
- 基于相似性的检索和自动标记是音乐信息检索的关键.
- 对于这些任务的人类监督信号是有限的,不能扩展.
- 自主监督学习 (SSL) 是有前途的,特别是自动标记的对比学习.
研究的目的:
- 用自我监督学习开发一种基于相似性的音乐检索的新型模型.
- 为音乐检索引入自主监督辅助损失的度量学习.
- 调查联合自我监督和监督培训的好处,而不是结模型.
主要方法:
- 提出了一种采用自主监督辅助损失的度量学习模型.
- 雇员兼职培训,包括自我监督和监督信号.
- 在微调阶段避免了模型结和数据增强.
主要成果:
- 拟议的方法大大提高了检索和标记性能指标.
- 在完全和部分可用性的人类注释标签的场景中观察到更好的性能.
- 同时训练和在微调过程中不进行增强产生了优异的结果.
结论:
- 这种新的自我监督度量学习方法有效地解决了基于相似性的检索挑战.
- 联合培训策略可以提高模型在音乐信息检索任务中的表现.
- 这些发现提供了一个可扩展的解决方案,用于音乐检索和自动标记,并具有有限的注释.
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