Perceptually motivated binaural beamforming with cues preservation for hearing aids

As'ad, Hala; Bouchard, M.; Kamkar-Parsi, Homayoun

doi:10.1109/ccece.2016.7726780

Cited by 5 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to preserve the binaural cues for directional interferers and diffuse-like noise components without a knowledge of the propagation model of the directional interferers, a binary decision/classifier algorithm common to the left and right beamformer outputs for each time-frequency (T-F) bin was proposed in [37], [38]. A challenge for this classification algorithm is its applicability in low input SNR environments, as most T-F bins can be classified as noise-dominant, resulting in low SNR improvement and an attenuated target output, as illustrated in [39]. As an attempt to enhance the performance of this method, the classification mechanism was later modified to use the output SNR instead of the input SNR [40].…”

Section: Introductionmentioning

confidence: 99%

A Robust Target Linearly Constrained Minimum Variance Beamformer With Spatial Cues Preservation for Binaural Hearing Aids

As'ad

Bouchard

Kamkar-Parsi

2019

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

View full text Add to dashboard Cite

In this paper, a binaural beamforming algorithm for hearing aid applications is introduced. The beamforming algorithm is designed to be robust to some error in the estimate of the target speaker direction. The algorithm has two main components: a robust target linearly constrained minimum variance (TLCMV) algorithm based on imposing two constraints around the estimated direction of the target signal, and a post-processor to help with the preservation of binaural cues. The robust TLCMV provides a good level of noise reduction and low level of target distortion under realistic conditions. The post-processor enhances the beamformer abilities to preserve the binaural cues for both diffuse-like background noise and directional interferers (competing speakers), while keeping a good level of noise reduction. The introduced algorithm does not require knowledge or estimation of the directional interferers' directions nor the second-order statistics of noise-only components. The introduced algorithm requires an estimate of the target speaker direction, but it is designed to be robust to some deviation from the estimated direction. Compared with recently proposed state-of-the-art methods, comprehensive evaluations are performed under complex realistic acoustic scenarios generated in both anechoic and mildly reverberant environments, considering a mismatch between estimated and true sources direction of arrival. Mismatch between the anechoic propagation models used for the design of the beamformers and the mildly reverberant propagation models used to generate the simulated directional signals is also considered. The results illustrate the robustness of the proposed algorithm to such mismatches. Index Terms-Robust LCMV, propagation model mismatch, steering vector mismatch, binaural cues preservations, noise reduction, binaural hearing aids.Hala As'ad received the M.A.Sc. degree in electrical engineering with a specialization in audio and speech processing in 2015 from the University of Ottawa, Ottawa, ON, Canada, where she is currently working toward the Ph.D. degree in electrical and computer engineering. Her doctoral research focuses on robust binaural beamforming, binaural cues preservation, and source direction of arrival detection in hearing aids. Her research interests include applied signal processing and machine learning with an emphasis on audio and speech processing, array signal processing, beamforming, speech enhancement, acoustic source localization, and hearing aids. She is the recipient of the Natural Sciences and Engineering Research Council Scholarship, the University of Ottawa Excellence Scholarship, and the Ontario Graduate Scholarship.

show abstract

Section: Introductionmentioning

confidence: 99%