8 research outputs found
Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights
Binaural beamforming algorithms for head-mounted assistive listening devices
are crucial to improve speech quality and speech intelligibility in noisy
environments, while maintaining the spatial impression of the acoustic scene.
While the well-known BMVDR beamformer is able to preserve the binaural cues of
one desired source, the BLCMV beamformer uses additional constraints to also
preserve the binaural cues of interfering sources. In this paper, we provide
theoretical and practical insights on how to optimally set the interference
scaling parameters in the BLCMV beamformer for an arbitrary number of
interfering sources. In addition, since in practice only a limited temporal
observation interval is available to estimate all required beamformer
quantities, we provide an experimental evaluation in a complex acoustic
scenario using measured impulse responses from hearing aids in a cafeteria for
different observation intervals. The results show that even rather short
observation intervals are sufficient to achieve a decent noise reduction
performance and that a proposed threshold on the optimal interference scaling
parameters leads to smaller binaural cue errors in practice.Comment: To appear in Proc. IWAENC 201
Overcoming DoF Limitation in Robust Beamforming: A Penalized Inequality-Constrained Approach
A well-known challenge in beamforming is how to optimally utilize the degrees
of freedom (DoF) of the array to design a robust beamformer, especially when
the array DoF is smaller than the number of sources in the environment. In this
paper, we leverage the tool of constrained convex optimization and propose a
penalized inequality-constrained minimum variance (P-ICMV) beamformer to
address this challenge. Specifically, we propose a beamformer with a
well-targeted objective function and inequality constraints to achieve the
design goals. The constraints on interferences penalize the maximum gain of the
beamformer at any interfering directions. This can efficiently mitigate the
total interference power regardless of whether the number of interfering
sources is less than the array DoF or not. Multiple robust constraints on the
target protection and interference suppression can be introduced to increase
the robustness of the beamformer against steering vector mismatch. By
integrating the noise reduction, interference suppression, and target
protection, the proposed formulation can efficiently obtain a robust beamformer
design while optimally trade off various design goals. When the array DoF is
fewer than the number of interferences, the proposed formulation can
effectively align the limited DoF to all of the sources to obtain the best
overall interference suppression. To numerically solve this problem, we
formulate the P-ICMV beamformer design as a convex second-order cone program
(SOCP) and propose a low complexity iterative algorithm based on the
alternating direction method of multipliers (ADMM). Three applications are
simulated to demonstrate the effectiveness of the proposed beamformer.Comment: submitted to IEEE Transactions on Signal Processin