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EnViTSA: 视觉变压器组合,用于声学事件分类的 SpecAugment.
Kian Ming Lim1, Chin Poo Lee1, Zhi Yang Lee2
1Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
使用视觉变压器和SpecAugment的EnViTSA通过减少过拟合和提高基准数据集的准确性来改善声学事件分类.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 声学事件分类 (AEC) 的深度学习模型因高度复杂性而面临过度拟合的挑战.
- 现有的方法在声学事件检测中扎着数据稀缺和泛化.
研究的目的:
- 引入EnViTSA,一种用于声学事件分类的创新方法.
- 通过减轻过度匹配和解决数据稀缺问题来提高AEC的性能.
主要方法:
- 预先训练的视觉转换器组合与 SpecAugment 数据增强相结合.
- 将原始声学信号转化为Log Mel频谱图.
- 通过 SpecAugment 应用时间和频率掩盖来生成合成训练数据.
主要成果:
- 在ESC-10上实现了93.50%的准确性,在ESC-50上达到85.85%,在UrbanSound8K上达到83.20%.
- 通过整体方法显著减少过度装配.
- 验证了 SpecAugment 对 AEC 数据增强的有效性.
结论:
- EnViTSA在声学事件分类方面取得了重大进展.
- 视觉变压器和SpecAugment显示出声学领域应用的巨大潜力.
- 提出的方法有效地解决了AEC深度学习的关键挑战.
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