# Sub-Band Knowledge Distillation Framework for Speech Enhancement

arXiv: https://arxiv.org/abs/2005.14435 · HTML: https://mlsanity.com/paper/2005.14435/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 7 of 7)

- [Two-Step Knowledge Distillation for Tiny Speech Enhancement](https://mlsanity.com/paper/2309.08144/) (2023)
- [Multi-View Attention Transfer for Efficient Speech Enhancement](https://mlsanity.com/paper/2208.10367/) (2022)
- [ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression](https://mlsanity.com/paper/2305.16665/) (2023)
- [Knowledge boosting during low-latency inference](https://mlsanity.com/paper/2407.11055/) (2024)
- [Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement](https://mlsanity.com/paper/2502.04711/) (2025)
- [Leveraging Local and Global Knowledge Integration with Time-Frequency Calibrated Distillation for Speech Enhancement](https://mlsanity.com/paper/2506.13127/) (2025)
- [DISPATCH: Distilling Selective Patches for Speech Enhancement](https://mlsanity.com/paper/2509.15922/) (2025)

## Abstract (arXiv)

In single-channel speech enhancement, methods based on full-band spectral features have been widely studied. However, only a few methods pay attention to non-full-band spectral features. In this paper, we explore a knowledge distillation framework based on sub-band spectral mapping for single-channel speech enhancement. Specifically, we divide the full frequency band into multiple sub-bands and pre-train an elite-level sub-band enhancement model (teacher model) for each sub-band. These teacher models are dedicated to processing their own sub-bands. Next, under the teacher models' guidance, we train a general sub-band enhancement model (student model) that works for all sub-bands. Without increasing the number of model parameters and computational complexity, the student model's performance is further improved. To evaluate our proposed method, we conducted a large number of experiments on an open-source data set. The final experimental results show that the guidance from the elite-level teacher models dramatically improves the student model's performance, which exceeds the full-band model by employing fewer parameters.