Binaural Speech Processing with Application to Hearing Devices

Doclo, Simon; Gannot, Sharon; Marquardt, Daniel; Hadad, Elior

doi:10.1002/9781119279860.ch18

Cited by 18 publications

(40 citation statements)

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“…where x, up and n are defined similarly as in (2) and v denotes the overall undesired component, i.e., interference plus background noise components. For the coherent desired source Sx and the coherent interfering sources Su,p, with p∈{1,...,P }, the vectors x and up can be written as…”

Section: Configuration and Notationmentioning

confidence: 99%

“…For head-mounted assistive listening devices (e.g., hearing aids, cochlear implants), algorithms that use the microphone signals from both the left and the right hearing device are effective techniques to improve speech intelligibility, as the spatial information captured by all microphones can be exploited [1,2]. Besides reducing undesired sources and limiting speech distortion, another important objective of binaural speech enhancement algorithms is the preservation of the listener's perception of the acoustical scene, in order to exploit the binaural hearing advantage [3] and to reduce confusions due to a mismatch between acoustical and visual information.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights

Gobling

Marquardt

Merks

et al. 2018

2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)

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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.

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Section: Configuration and Notationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights

Gobling

Marquardt

Merks

et al. 2018

2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…where the speech vector x and the noise vector n are defined similarly as in (2). Without loss of generality, the first microphone arXiv:1908.04848v1 [eess.AS] 13 Aug 2019 on each device is chosen as the reference microphone, i.e.…”

Section: Configuration and Notationmentioning

confidence: 99%

“…The BMVDR [2,15] aims at minimizing the output noise PSD while preserving the desired speech component in the reference microphone signals (xL and xR), hence preserving the binaural cues of the desired speech source. The optimization problem for the left filter vector wL is given by…”

Section: Binaural Mvdr Beamformermentioning

confidence: 99%

“…As a well-known example, the binaural minimum-variance distortionless-response (BMVDR) beamformer is able to preserve the binaural cues (i.e. the interaural time and level differences) of a desired speech source [1][2][3]. For a moving speech source in a reverberant environment, the BMVDR can be steered using the relative transfer functions (RTFs) [4], which relate the acoustic transfer functions between the desired speech source and all microphones to the so-called reference microphones.…”

Section: Introductionmentioning

confidence: 99%

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RTF-Steered Binaural MVDR Beamforming Incorporating Multiple External Microphones

Gosling

Middelberg

Doclo

2019

2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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The binaural minimum-variance distortionless-response (BMVDR) beamformer is a well-known noise reduction algorithm that can be steered using the relative transfer function (RTF) vector of the desired speech source. Exploiting the availability of an external microphone that is spatially separated from the head-mounted microphones, an efficient method has been recently proposed to estimate the RTF vector in a diffuse noise field. When multiple external microphones are available, different RTF vector estimates can be obtained by using this method for each external microphone. In this paper, we propose several procedures to combine these RTF vector estimates, either by selecting the estimate corresponding to the highest input SNR, by averaging the estimates or by combining the estimates in order to maximize the output SNR of the BMVDR beamformer. Experimental results for a moving speaker and diffuse noise in a reverberant environment show that the output SNR-maximizing combination yields the largest binaural SNR improvement and also outperforms the state-of-the art covariance whitening method.

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Robust parameter strategy for Wiener-based binaural noise reduction methods in hearing aids

Carmo

Borsoi

Costa

2022

Biomedical Signal Processing and Control

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Binaural Speech Processing with Application to Hearing Devices

Cited by 18 publications

References 50 publications

Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights

Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights

RTF-Steered Binaural MVDR Beamforming Incorporating Multiple External Microphones

Robust parameter strategy for Wiener-based binaural noise reduction methods in hearing aids

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