HRTF-based Robust Least-Squares Frequency-Invariant Polynomial Beamforming
This work addresses robust beamforming for robot audition, but it appears incremental as it builds on a previously proposed design.
The authors tackled the problem of robust beamforming for robot audition by proposing an HRTF-based polynomial beamformer design that accounts for a humanoid robot's head's influence on sound and allows flexible steering, with results confirming its effectiveness through signal-independent measures and word error rates in speech recognition.
In this work, we propose a robust Head-Related Transfer Function (HRTF)-based polynomial beamformer design which accounts for the influence of a humanoid robot's head on the sound field. In addition, it allows for a flexible steering of our previously proposed robust HRTF-based beamformer design. We evaluate the HRTF-based polynomial beamformer design and compare it to the original HRTF-based beamformer design by means of signal-independent measures as well as word error rates of an off-the-shelf speech recognition system. Our results confirm the effectiveness of the polynomial beamformer design, which makes it a promising approach to robust beamforming for robot audition.