A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

Reindl, Klaus; Zheng, Yuanhang; Schwarz, Andreas; Meier, Stefan; Maas, Roland; Sehr, Armin; Kellermann, Walter

doi:10.1016/j.csl.2012.07.011

Cited by 35 publications

(28 citation statements)

References 18 publications

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“…The actual noise PSD is obtained similar to (11) and the smoothing factor α was set to 0.8 here and throughout the experiment. The LogErr measures which is averaged over different noises are presented in Fig.…”

Section: Noise Psd Estimationmentioning

confidence: 99%

“…In [10] it has been proposed to use the blind source separation (BSS) for canceling the target speech in a diffuse noise field and consequently to estimate the noise power. Moreover, the same approach as [10] has been employed in [11] in order to estimate the reverberation tail which was considered as a diffuse noise. A correction gain based on the BSS de-mixing matrix was derived to reduce the bias of the estimated noise PSD.…”

Section: Introductionmentioning

confidence: 99%

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Fast noise PSD estimation based on blind channel identification

Azarpour

Enzner

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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This paper introduces a new binaural noise estimator based on the target cancellation technique. The left and right source-tomicrophone transfer functions (channels) are blindly estimated by means of the constrained least-mean-square algorithm, minimizing the cross-relation error between left and right microphone signals. This blind channel identification (BCI) thus implies a blocking of the target signal and a biased noise estimation in the error signal. The related noise power is then corrected using the estimated channels. The performance of the proposed algorithm is investigated in comparison to different single and dual channel noise power estimators. The investigations show that the proposed algorithm is capable of estimating and tracking the noise power fast and accurately. The suitability of the noise power spectral density (PSD) estimator is finally confirmed within a speech enhancement framework.

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Section: Noise Psd Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast noise PSD estimation based on blind channel identification

Azarpour

Enzner

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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“…Therefore, Wienertype spectral enhancement filters based on the obtained noise estimate are used for the noise reduction unit. The real-valued spectral weights for frequency at output channel p are given by [56] …”

Section: A Two-unit Scheme: Bm Plus Spectral Enhancement Filtersmentioning

confidence: 99%

“…A bias correction function is proposed based on an assumed coherence [21,56]. If the noise field is approximated as spherically isotropic [49], the theoretical noise coherence function reads:…”

Section: Improved Noise Estimate By Assuming An Ideal Diffuse Noise Fmentioning

confidence: 99%

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

Zheng

Reindl

Kellermann

2014

EURASIP J. Adv. Signal Process.

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For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, 'Directional Blind Source Separation (BSS)' was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated.

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“…For instance, it has been proposed to use the blind source separation (BSS) approach for canceling the target speech components in a diffuse noise field and consequently to estimate the noise power at the output of the blocking system [39]. Later, the same approach was employed in [40] to estimate the reverberation tail, which is considered as diffuse noise. A spectral correction gain function based on the BSS de-mixing matrix was derived to reduce the bias of the estimated noise PSD.…”

Section: Introductionmentioning

confidence: 99%

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

Azarpour

Enzner

2017

EURASIP J. Adv. Signal Process.

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Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.

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A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

Cited by 35 publications

References 18 publications

Fast noise PSD estimation based on blind channel identification

Fast noise PSD estimation based on blind channel identification

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

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