只在教师表现更好的频谱区域进行知识蒸馏，提升语音增强模型压缩效果。
DISPATCH: Distilling Selective Patches for Speech Enhancement
- 按教师优于学生的频谱块选择蒸馏区域，避免无效模仿。
- 引入多尺度频段自适应块大小，匹配不同频带特性。
- 适用于追求轻量化语音增强模型的开发者与工程师。
在语音增强中，知识蒸馏（KD）通过将高性能教师模型的知识迁移到紧凑的学生模型来压缩模型。然而，传统方法强制学生完全模仿教师输出，导致学生被迫学习教师表现差的区域，并在已表现良好的区域重复蒸馏，提升有限。本文提出一种名为选择性频谱块知识蒸馏（DISPatch）的框架，根据知识差距评分，仅在教师优于学生的频谱块上应用蒸馏损失。该方法引导优化聚焦于学生最需改进的区域，同时减少教师不可靠指导的影响。此外，提出多尺度选择性块（MSSP），在低频和高频段使用不同块大小以适应频谱异质性。将DISPatch融入常规KD方法后，紧凑学生模型性能持续提升；进一步结合状态领先频域依赖型KD方法，所有指标均显著改善。
原文摘要 · Abstract (English)
In speech enhancement, knowledge distillation (KD) compresses models by transferring a high-capacity teacher's knowledge to a compact student. However, conventional KD methods train the student to mimic the teacher's output entirely, which forces the student to imitate the regions where the teacher performs poorly and to apply distillation to the regions where the student already performs well, which yields only marginal gains. We propose Distilling Selective Patches (DISPatch), a KD framework for speech enhancement that applies the distillation loss to spectrogram patches where the teacher outperforms the student, as determined by a Knowledge Gap Score. This approach guides optimization toward areas with the most significant potential for student improvement while minimizing the influence of regions where the teacher may provide unreliable instruction. Furthermore, we introduce Multi-Scale Selective Patches (MSSP), a frequency-dependent method that uses different patch sizes across low- and high-frequency bands to account for spectral heterogeneity. We incorporate DISPatch into conventional KD methods and observe consistent gains in compact students. Moreover, integrating DISPatch and MSSP into a state-of-the-art frequency-dependent KD method considerably improves performance across all metrics.
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