Sub-Band Knowledge Distillation Framework for Speech Enhancement

• DOI: 10.21437/interspeech.2020-1539
• Publication Date: 2020-10-27T09:22:11Z
• Authors: Xiang Hao; Shixue Wen; Xiangdong Su; Yun Liu; Guanglai Gao; Xiaofei Li
• Keywords: FOS: Computer and information sciences; Sound (cs.SD); 03 medical and health sciences; Computer Science - Computation and Language; Audio and Speech Processing (eess.AS); FOS: Electrical engineering, electronic engineering, information engineering; 0305 other medical science; Computation and Language (cs.CL); Computer Science - Sound; Electrical Engineering and Systems Science - Audio and Speech Processing

Published in Interspeech 2020<br/>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.<br/>