2021/06/22 by Andong Li, Chengshi Zheng, Li, Andong +5 · 2 citations
Computer Science · Neuroscience · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Hearing Loss and Rehabilitation #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.11789
openalex publication_date 2021/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The capability of the human to pay attention to both coarse and fine-grained regions has been applied to computer vision tasks. Motivated by that, we propose a collaborative learning framework in the complex domain for monaural noise suppression. The proposed system consists of two principal modules, namely spectral feature extraction module (FEM) and stacked glance-gaze modules (GGMs). In FEM, the UNet-block is introduced after each convolution layer, enabling the feature recalibration from multiple scales. In each GGM, we decompose the multi-target optimization in the complex spectrum into two sub-tasks. Specifically, the glance path aims to suppress the noise in the magnitude domain to obtain a coarse estimation, and meanwhile, the gaze path attempts to compensate for the lost spectral detail in the complex domain. The two paths work collaboratively and facilitate spectral estimation from complementary perspectives. Besides, by repeatedly unfolding the GGMs, the intermediate result can be iteratively refined across stages and lead to the ultimate estimation of the spectrum. The experiments are conducted on the WSJ0-SI84, DNS-Challenge dataset, and Voicebank+Demand dataset. Results show that the proposed approach achieves state-of-the-art performance over previous advanced systems on the WSJ0-SI84 and DNS-Challenge dataset, and meanwhile, competitive performance is achieved on the Voicebank+Demand corpus.