CMKD:CNN/基于变压器的音频分类跨模型知识蒸
IEEE transactions on pattern analysis and machine intelligence
|December 17, 2025
概括
卷积神经网络 (CNN) 和音频谱变压器 (AST) 通过交叉模型知识蒸相互改善. 这种方法提高了学生模型的性能,在音频分类任务中经常超过教师模型.
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:
- 卷积神经网络 (CNN) 已经主导了音频分类.
- 音频谱图变压器 (AST) 最近出现,性能优于CNN.
- 知识蒸 (KD) 是一种模型培训技术.
研究的目的:
- 调查CNN和AST之间的相互作用.
- 在音频分类中应用跨模型知识蒸 (CMKD).
- 使用建议的CMKD方法实现最先进的结果.
主要方法:
- 利用CNN和AST作为教师和学生.
- 实现知识蒸 (KD) 进行模型间的知识传输.
- 在基准数据集上评估了CNN/变压器跨模型知识蒸 (CMKD) 方法.
主要成果:
- 通过KD培训的学生模型显著提高了绩效.
- 在许多情况下,学生模型的表现优于其教师模型.
- 在FSD50K,AudioSet和ESC-50数据集上取得了新的最先进的结果.
结论:
- 在音频分类中,CNN和AST表现出一种互补的关系.
- 跨模型知识蒸 (CMKD) 有效地提高模型性能.
- 拟议的CMKD方法为未来的音频分类研究提供了一个有希望的方向.
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