2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDAUD-24.3
Paper Title LEARNING DISENTANGLED FEATURE REPRESENTATIONS FOR SPEECH ENHANCEMENT VIA ADVERSARIAL TRAINING
Authors Nana Hou, Nanyang Technological University, Singapore; Chenglin Xu, National University of Singapore, Singapore; Eng Siong Chng, Nanyang Technological University, Singapore; Haizhou Li, National University of Singapore, Singapore
SessionAUD-24: Signal Enhancement and Restoration 1: Deep Learning
LocationGather.Town
Session Time:Thursday, 10 June, 16:30 - 17:15
Presentation Time:Thursday, 10 June, 16:30 - 17:15
Presentation Poster
Topic Audio and Acoustic Signal Processing: [AUD-SEN] Signal Enhancement and Restoration
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Deep learning based speech enhancement degrades significantly in face of unseen noise. To address such mismatch, in this work, we propose to learn noise-agnostic feature representations by disentanglement learning, which removes the unspecified noise factor, while keeping the specified factors of variation associated with the clean speech. Specifically, a discriminator module is introduced to distinguish the type of noises, which is referred to as the disentangler. With the adversarial training strategy, a gradient reversal layer seeks to disentangle the noise factor and remove it from the feature representation. Experiment results show that the proposed approach achieves 5.8% and 5.2% relative improvements over the best baseline in terms of perceptual evaluation of the speech quality (PESQ) and segmental signal-to-noise ratio (SSNR), respectively. Furthermore, the ablation study indicates that the proposed disentangler module is also effective in the other encoder-decoder-like structure. The scripts are available at Github.