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 IDSPE-51.3
Paper Title Neural Kalman Filtering for Speech Enhancement
Authors Wei Xue, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, Bowen Zhou, JD AI Research, China
SessionSPE-51: Speech Enhancement 7: Single-channel Processing
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Conventional learning-based speech enhancement methods usually utilize existing building blocks to design the deep neural networks (DNNs), while how to effectively integrate the statistical signal processing based schemes, which are expert-knowledge driven and could ameliorate the over-fitting problem, into the network design remains an open issue. In this paper, we extend the conventional Kalman filtering (KF) and propose a supervised-learning based neural Kalman filter (NKF) for speech enhancement. Similar to KF, the proposed method first obtains a prediction from the speech evolution model and then integrates the short-term instantaneous observation by linear weighting, and the weights are calculated by comparing between the speech prediction residual error and the environmental noise level. An end-to-end network is designed to convert the speech linear prediction model in KF to non-linear, and to compact all other conventional linear filtering operations. Different with other DNN based methods, the proposed method provides a specialized network design inspired from the conventional signal processing, the backpropagation can be directly applied on the linear filtering operations integrated from KF. We conduct experiments in different noisy conditions, and the results demonstrate that the proposed method outperforms the baseline methods which are based on either signal processing or DNNs.