Adaptive Multi-Channel Signal Enhancement Based on Multi-Source Contribution Estimation

Jacob Donley, Vladimir Tourbabin, Boaz Rafaely, Ravish Mehra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Automated solutions to multi-channel signal enhancement for improving speech communication in noisy environments has become a popular goal among the research community. Many proposed approaches focus on adapting to speech signals based on their temporal characteristics but these methods are primarily limited to specific types of desired and undesired sound sources. This paper outlines a new method to adapt to desired and undesired signals using their spatial statistics, independent of their temporal characteristics. The method uses a linearly constrained minimum variance (LCMV) beamformer to estimate the relative source contribution of each source in a mixture, which is then used to weight statistical estimates of the spatial characteristics of each source used for final separation. The proposed method allows for instantaneous desired and undesired source selection, a useful ability for the enhancement of conversations. The simulated results show that the method can adapt to the targeted source in noisy mixture signals and that under realistic conditions it is also capable of reaching ideal MVDR performance.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797060
StatePublished - 8 Dec 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference29th European Signal Processing Conference, EUSIPCO 2021


  • Adaptive beam-forming
  • Microphone array
  • Multi-channel processing
  • Parameter estimation
  • Signal enhancement

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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