By Topic

Multi-Microphone Noise Reduction Based on Orthogonal Noise Signal Decompositions

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
$31 $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

2 Author(s)
Habets, E.A.P. ; Int. Audio Labs. Erlangen, Univ. of Erlangen-Nuremberg, Erlangen, Germany ; Benesty, J.

Multi-microphone noise reduction plays an increasing and important role in acoustic communication systems. Existing multichannel noise reduction filters are commonly computed based on a single noise covariance matrix. Recently, an orthogonal noise signal decomposition was proposed that uses a single noise signal as a reference. Using this decomposition, it was possible to reformulate the noise reduction problem and derived a multichannel noise reduction filter that allows a tradeoff between the noise that is coherent and incoherent with respect to the reference signal. In this contribution, we analyze the previously proposed decomposition and propose an orthogonal decomposition that is based on a rank-one projection of all noise signals. The projection is chosen such that the total variance of the coherent noise component is maximized. To further improve the separation between coherent noise and incoherent noise, a rank-Q projection of the observed noise signals is proposed. The decomposed noise covariance matrix is then used to derive a minimum variance distortionless response beamformer that allows a tradeoff between coherent and incoherent noise reduction, and to form a constraint matrix for a linearly constrained minimum variance beamformer. The results of the performance evaluation demonstrate the advantage of the proposed decompositions over the previously proposed decomposition.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:21 ,  Issue: 6 )