HRTF-based Robust Least-Squares Frequency-Invariant Polynomial Beamforming

TL;DR

This paper introduces a robust HRTF-based polynomial beamformer that accounts for head influence on sound, offering flexible steering and improved robustness for robot audition, validated through signal measures and speech recognition performance.

Contribution

It presents a novel polynomial beamformer design that enhances robustness and steering flexibility in HRTF-based beamforming for humanoid robots.

Findings

Polynomial beamformer outperforms original design in signal measures

Improved word error rates in speech recognition tasks

Effective handling of head influence on sound field

Abstract

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.