Acoustic Beamforming for Hearing Aid Applications

Doclo, Simon; Gannot, Sharon; Moonen, Marc; Spriet, Ann

doi:10.1002/9780470487068.ch9

Cited by 139 publications

(113 citation statements)

References 86 publications

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“…The proposed enhancement algorithm is implemented using the well-known binaural minimum variance distortionless response (MVDR) beamformer [9,11], coupled with an SNR estimator based on the assumption that the background noise is isotropic [19]. In subjective evaluations using measurements of the speech reception threshold (SRT), we find that the overall enhancement performance is equivalent to other cue-preserving binaural algorithms such as MVDR with partial noise estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Methods such as fixed and adaptive beamforming and multichannel Wiener filtering are generally capable of better noise suppression and lower speech distortion than single-channel methods [9,11]. In the case of bilateral hearing loss, a hearing aid is required at both ears.…”

Section: Introductionmentioning

confidence: 99%

“…Miniaturization has allowed modern hearing aids to utilize multiple microphones in compact devices, allowing the use of multimicrophone signal enhancement algorithms [9][10][11][12]. Methods such as fixed and adaptive beamforming and multichannel Wiener filtering are generally capable of better noise suppression and lower speech distortion than single-channel methods [9,11].…”

Section: Introductionmentioning

confidence: 99%

“…By applying the same gain to both sides, these methods preserve all differences (both level and temporal) between the reference microphone signals but may suffer in noise reduction performance since they are effectively single-channel noise reduction methods, i.e., the signal at the left ear is generated by applying a spectrotemporal gain to the left reference microphone signal while the signal at the right ear is generated by applying the same spectro-temporal gain to the right microphone signal. The second category uses a more general approach, combining spectral and spatial filtering [9,17,18] based on a cost function which allows for controlling the amount of interaural cue distortion. Although typically a larger noise reduction can be achieved, there is always a trade-off between noise reduction performance and interaural cue preservation for interfering sources and the background noise.…”

Section: Introductionmentioning

confidence: 99%

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Speech enhancement for multimicrophone binaural hearing aids aiming to preserve the spatial auditory scene

Thiemann

Müller

Marquardt

et al. 2016

EURASIP J. Adv. Signal Process.

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Modern binaural hearing aids utilize multimicrophone speech enhancement algorithms to enhance signals in terms of signal-to-noise ratio, but they may distort the interaural cues that allow the user to localize sources, in particular, suppressed interfering sources or background noise. In this paper, we present a novel algorithm that enhances the target signal while aiming to maintain the correct spatial rendering of both the target signal as well as the background noise. We use a bimodal approach, where a signal-to-noise ratio (SNR) estimator controls a binary decision mask, switching between the output signals of a binaural minimum variance distortionless response (MVDR) beamformer and scaled reference microphone signals. We show that the proposed selective binaural beamformer (SBB) can enhance the target signal while maintaining the overall spatial rendering of the acoustic scene.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Speech enhancement for multimicrophone binaural hearing aids aiming to preserve the spatial auditory scene

Thiemann

Müller

Marquardt

et al. 2016

EURASIP J. Adv. Signal Process.

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“…It is more convenient to write the STFT-domain microphone signals in a vector notation: (3) where and the superscript is the transpose operator. In the following we assume that for all .…”

Section: Problem Formulationmentioning

confidence: 99%

Multi-Microphone Noise Reduction Based on Orthogonal Noise Signal Decompositions

Habets

Benesty

2013

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Multi-microphone noise reduction plays an increasing and important role in acoustic communication systems. Existing multichannel noise reduction filters are commonly computed based on a single noise covariance matrix. Recently, an orthogonal noise signal decomposition was proposed that uses a single noise signal as a reference. Using this decomposition, it was possible to reformulate the noise reduction problem and derived a multichannel noise reduction filter that allows a tradeoff between the noise that is coherent and incoherent with respect to the reference signal. In this contribution, we analyze the previously proposed decomposition and propose an orthogonal decomposition that is based on a rank-one projection of all noise signals. The projection is chosen such that the total variance of the coherent noise component is maximized. To further improve the separation between coherent noise and incoherent noise, a rank-projection of the observed noise signals is proposed. The decomposed noise covariance matrix is then used to derive a minimum variance distortionless response beamformer that allows a tradeoff between coherent and incoherent noise reduction, and to form a constraint matrix for a linearly constrained minimum variance beamformer. The results of the performance evaluation demonstrate the advantage of the proposed decompositions over the previously proposed decomposition.

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