Coherence-assisted Wiener filter binaural speech enhancement

Laska, Brady; Bolić, Miodrag; Goubran, Rafik

doi:10.1109/imtc.2010.5488166

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…In practice, the PSDs of the noisy input signals are obtained using a first-order recursive averaging filter [22,25,26],…”

Section: Input Signal Modelmentioning

confidence: 99%

“…It is well-known that spatial coherence between two omnidirectional microphones in a spherically isotropic field can be modeled as real-valued analytic sinc function. In subsequent studies, several coherence models for binaural noise field considering the shadowing effect of the head have been proposed [22,37,38]. In this paper, we use the sinc function Γ N = sinc(2πfd LR /c), where d LR and c are the distance between the left and right microphones and the speed of sound, respectively, to model the coherence in the diffuse noise field.…”

Section: Noise Psd Estimation Based On Eigenvaluesmentioning

confidence: 99%

“…When the auto-and cross-PSDs of the input signal are estimated using the first-order recursion algorithm in (5), the smoothing factor, α, has to cope with two contradictory constraints: capturing the time-varying statistics of the signal component and reducing the estimator variance [22,26,43]. When the noise statistics are fast time varying, capturing of the instantaneous statistics of the signals is necessary.…”

Section: Compensation For Underestimation Of Noise Psdmentioning

confidence: 99%

“…The auto-and cross-PSDs of the input binaural signal are often estimated using a first-order recursive averaging filter [22]. In a rapidly changing noise environment, averaging with a short time constant is required to quickly reflect the signal statistics of the signal PSDs.…”

Section: Introductionmentioning

confidence: 99%

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Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Baek

Park

2017

J AUDIO SPEECH MUSIC PROC.

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In speech enhancement, noise power spectral density (PSD) estimation plays a key role in determining appropriate de-nosing gains. In this paper, we propose a robust noise PSD estimator for binaural speech enhancement in timevarying noise environments. First, it is shown that the noise PSD can be numerically obtained using an eigenvalue of the input covariance matrix. A simplified estimator is then derived through an approximation process, so that the noise PSD is expressed as a combination of the second eigenvalue of the input covariance matrix, the noise coherence, and the interaural phase difference (IPD) of the input signal. Later, to enhance the accuracy of the noise PSD estimate in time-varying noise environments, an eigenvalue compensation scheme is presented, in which two eigenvalues obtained in noise-dominant regions are combined using a weighting parameter based on the speech presence probability (SPP). Compared with the previous prediction filter-based approach, the proposed method requires neither causality delays nor explicit estimation of the prediction errors. Finally, the proposed noise PSD estimator is applied to a binaural speech enhancement system, and its performance is evaluated through computer simulations. The simulation results show that the proposed noise PSD estimator yields accurate noise PSD regardless of the direction of the target speech signal. Therefore, slightly better performance in quality and intelligibility can be obtained than that with conventional algorithms.

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“…In practice, the PSDs of the noisy input signals are obtained using a first-order recursive averaging filter [22,25,26],…”

Section: Input Signal Modelmentioning

confidence: 99%

Section: Noise Psd Estimation Based On Eigenvaluesmentioning

confidence: 99%

Section: Compensation For Underestimation Of Noise Psdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Baek

Park

2017

J AUDIO SPEECH MUSIC PROC.

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“…기술이 제안되었고 [7,8] 이를 통해 더 높은 잡음 제거율 과 낮은 음성 왜곡률을 달성 할 수 있음을 보였다. [8] 또한 잡음 파워 스펙트럼 추정 단계에서 상관도 정보를 활용한 기술도 발표되었다.…”

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On-line noise coherence estimation algorithm for binaural speech enhancement system

Ji¹,

Baek²,

Park³

2016

The Journal of the Acoustical Society of Korea

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In this paper, an on-line noise coherence estimation algorithm for binaural speech enhancement system is proposed. A number of noise Power Spectral Density (PSD) estimation algorithms based on the noise coherence between two microphones have been proposed to improve the speech enhancement performance. In the conventional algorithms, the noise coherence was characterized using a real-valued analytic model. However, unlike the analytic model, the noise coherence between the two microphones is time-varying in real environments. Thus, in this paper, the noise coherence is updated in accordance with the variation of the acoustic environment to track the realistic noise coherence. The noise coherence can be updated only during the absence of speech, and the simulation results demonstrate the superiority of the proposed algorithm over the conventional algorithms based on the analytic model.

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Improving the performance of cough sound discriminator in reverberant environments using microphone array

Moradshahi

Chatrzarrin

Goubran

2012

2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings

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Coherence-assisted Wiener filter binaural speech enhancement

Cited by 8 publications

References 14 publications

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

Robust noise power spectral density estimation for binaural speech enhancement in time-varying diffuse noise field

On-line noise coherence estimation algorithm for binaural speech enhancement system

Improving the performance of cough sound discriminator in reverberant environments using microphone array

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