By Topic

Multi-channel noise reduction with beamforming and masking-based Wiener filtering for human-robot interface

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Jungpyo Hong ; Dept. of Electr. Eng., KAIST, Daejeon, South Korea ; Keunseok Cho ; Minsoo Hahn ; Suhwan Kim
more authors

In this paper, an efficient noise reduction algorithm is proposed for robust speech recognition. For the nonstationary noise reduction, frequency-domain beamforming-based speech enhancement is performed and masking-based Wiener filter is applied to the beamforming output. To design the masking-based Wiener filter, the spectrum of beamforming output is classified into noise spectrum and speech spectrum at each spectral bin by the inter-channel time delay between two reference inputs. Hamming windowing for the speech spectrum and noise spectrum is separately performed to smooth each spectrum. Then, the Wiener filtering is applied to the beamforming output. The performance of the proposed algorithm significantly improves the speech recognition accuracies and the signal-to-noise ratios.

Published in:

Automation, Robotics and Applications (ICARA), 2011 5th International Conference on

Date of Conference:

6-8 Dec. 2011