Neural Kalman Filtering for Speech Enhancement

Wei Xue, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, Bowen Zhou

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings
PublisherIEEE
Pages7108-7112
Number of pages5
ISBN (Electronic)9781728176055
ISBN (Print)9781728176062
DOIs
Publication statusPublished - 11 Jun 2021
Event2021 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021 - Virtual, Toronto, ON, Canada
Duration: 6 Jun 202111 Jun 2021
https://www.2021.ieeeicassp.org/2021.ieeeicassp.org/index.html
https://ieeexplore.ieee.org/xpl/conhome/9413349/proceeding

Publication series

NameProceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021
Country/TerritoryCanada
CityVirtual, Toronto, ON
Period6/06/2111/06/21
Internet address

User-Defined Keywords

  • Maximum likelihood detection
  • Filtering
  • Working environment noise
  • Neural networks
  • Nonlinear filters
  • Speech enhancement
  • Signal processing

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