ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
Abstract
Noise suppression (NS) models have been widely applied to enhance speech quality. Recently, Deep Learning-Based NS, which we denote as Deep Noise Suppression (DNS), became the mainstream NS method due to its excelling performance over traditional ones. However, DNS models face 2 major challenges for supporting the real-world applications. First, high-performing DNS models are usually large in size, causing deployment difficulties. Second, DNS models require extensive training data, including noisy audios as inputs and clean audios as labels. It is often difficult to obtain clean labels for training DNS models. We propose the use of knowledge distillation (KD) to resolve both challenges. Our study serves 2 main purposes. To begin with, we are among the first to comprehensively investigate mainstream KD techniques on DNS models to resolve the two challenges. Furthermore, we propose a novel Attention-Based-Compression KD method that outperforms all investigated mainstream KD frameworks on DNS task.
Cite
@article{arxiv.2305.16665,
title = {ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression},
author = {Yixin Wan and Yuan Zhou and Xiulian Peng and Kai-Wei Chang and Yan Lu},
journal= {arXiv preprint arXiv:2305.16665},
year = {2023}
}
Comments
This paper was accepted to Interspeech 2023 Main Conference