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Design of Robust Two-dimensional Polynomial Beamformers as a Convex Optimization Problem with Application to Robot Audition by Barfuss, Hendrik; Bachmann, Markus; Buerger, Michael; Schneider, Martin; Kellerman, Walter is a scholarly article available to read on EtoBox.

What is Design of Robust Two-dimensional Polynomial Beamformers as a Convex Optimization Problem with Application to Robot Audition about?

We propose a robust two-dimensional polynomial beamformer design method, formulated as a convex optimization problem, which allows for flexible steering of a previously proposed data-independent robust beamformer in both azimuth and elevation direction.~As an exemplary application, the proposed two-dimensional polynomial beamformer design is applied to a twelve-element microphone array, integrated into the head of a humanoid robot. To account for the effects of the robot's head on the sound field, measured head-related transfer functions are integrated into the optimization problem as steering vectors. The two-dimensional polynomial beamformer design is evaluated using signal-independent and signal-dependent measures. The results confirm that the proposed polynomial beamformer design approximates the original fixed beamformer design very accurately, which makes it an attractive approach for robust real-time data-independent beamforming.

Author

Barfuss, Hendrik; Bachmann, Markus; Buerger, Michael; Schneider, Martin; Kellerman, Walter

Published

2017

Language

EN