Enhancement of speech degraded by coherent and incoherent noise using a cross-spectral estimator

Bouquin-Jeannes, R. La; Akbari, Ahmad; Faucon, G.

doi:10.1109/89.622576

Cited by 80 publications

(23 citation statements)

References 8 publications

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“…Exploiting Hermitian symmetry by computing by means of (11) we obtain (17)- (18), shown at the bottom of the page, with . From (18) we see that the real parts of the two terms in in are completely removed when estimating by means of (11). To further reduce the effect of the imaginary part of on the estimate , the smoothing parameter can be increased during periods where the signal-to-noise ratio (SNR) is rather high.…”

Section: A Reduction Of the Estimation Errors Andmentioning

confidence: 94%

Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement

Hendriks

Gerkmann

2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-For multi-channel noise reduction algorithms like the minimum variance distortionless response (MVDR) beamformer, or the multi-channel Wiener filter, an estimate of the noise correlation matrix is needed. For its estimation, it is often proposed in the literature to use a voice activity detector (VAD). However, using a VAD the estimated matrix can only be updated in speech absence. As a result, during speech presence the noise correlation matrix estimate does not follow changing noise fields with an appropriate accuracy. This effect is further increased, as in nonstationary noise voice activity detection is a rather difficult task, and false-alarms are likely to occur. In this paper, we present and analyze an algorithm that estimates the noise correlation matrix without using a VAD. This algorithm is based on measuring the correlation of the noisy input and a noise reference which can be obtained, e.g., by steering a null towards the target source. When applied in combination with an MVDR beamformer, it is shown that the proposed noise correlation matrix estimate results in a more accurate beamformer response, a larger signal-to-noise ratio improvement and a larger instrumentally predicted speech intelligibility when compared to competing algorithms such as the generalized sidelobe canceler, a VAD-based MVDR beamformer, and an MVDR based on the noisy correlation matrix.

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Section: A Reduction Of the Estimation Errors Andmentioning

confidence: 94%

Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement

Hendriks

Gerkmann

2012

IEEE Trans. Audio Speech Lang. Process.

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“…One of the main challenges for noise suppression algorithms methods is the detection or the estimation of the signal of interest. For example, the BSS techniques have shown a good performances that makes the hands-free system in a car communication highly robust toward noise components [22][23][24][25][26][27][28][29][30][31][32]. It is well known that the BSS is a powerful technique for acoustic noise reduction and speech enhancement in many situation such as in a car configuration involving loosely spaced microphones and short impulse responses.…”

Section: Introductionmentioning

confidence: 99%

A new regularized forward blind source separation algorithm for automatic speech quality enhancement

Zoulikha

Djendi

2016

Applied Acoustics

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“…The CPSD-based approach (Zelinski, 1988(Zelinski, , 1990, the adaptive noise canceller (ANC) approach (Maj et al, 2006), (Berghe & Wooters, 1998), and the CSS-based approach (Guerin et al, 2003;Le Bouquin-Jannès et al, 1997;Zhang & Jia, 2005) are well-known examples. The former lacks robustness in a number of practical noise fields (i.e., coherent noise).…”

Section: State Of the Artmentioning

confidence: 99%