Noise cross PSD estimation using phase information in diffuse noise field

Rahmani, Mostafa; Akbari, Ahmad; Ayad, Beghdad; Lithgow, Brian

doi:10.1016/j.sigpro.2008.10.020

Cited by 23 publications

(15 citation statements)

References 7 publications

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“…For gain function, we employ the (square-root) Wiener filter defined as (20) In the following, we describe the implementation details of the proposed coherence-based method. The two signals captured by the microphones are first processed in 20-ms frames with a Hanning window and a 50% overlap between adjacent frames.…”

Section: B Proposed Speech Enchantment Algorithm and Implementationmentioning

confidence: 99%

“…In other studies, such as [19], the coherence function has been used as a measure to assess distortion (i.e., all nonlinear effects and noise) in hearing aids. The authors in [20] employed the coherence function for noise estimation in diffuse noise fields. In [21], [22], we proposed two coherence-based dual-microphone noise reduction methods and showed that they offer significant improvement over a well-established beamformer in terms of intelligibility.…”

Section: Introductionmentioning

confidence: 99%

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A Dual-Microphone Algorithm That Can Cope With Competing-Talker Scenarios

Yousefian

Loizou

2013

IEEE Trans. Audio Speech Lang. Process.

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Abstract-This paper introduces a novel technique for signal-to-noise ratio (SNR) estimation for scenarios where two closely-spaced microphones are available. The proposed technique utilizes the real and imaginary parts of the coherence function between the input signals to estimate the SNR without assuming prior knowledge of the noise statistics. The corresponding dual-microphone speech enhancement algorithm utilizes a Wiener filter as a gain function constructed using the SNR values computed by the coherence function. Since the proposed SNR estimation technique does not require access to noise statistics, it can be applied in situations where interfering speakers are present. An adaptive speech reception threshold (SRT) test was used to assess the intelligibility of speech processed by the proposed algorithm in scenarios where one or two interfering talkers were present in anechoic and reverberant conditions. Intelligibility listening tests were conducted with both normal-hearing (NH) and cochlear implant (CI) listeners. Results revealed significant improvements in intelligibility and quality over a (baseline) fixed directional algorithm and a well-established beamformer algorithm. In a nearly anechoic room with competing talkers, the improvement in SRT obtained relative to the directional microphone ranged from 5-10 dB, while the improvement obtained by the beamformer was about 2 dB. In reverberant environments, the improvement in SRT remained high (4-7 dB) at ms, and decreased to 1-2 dB at ms. Overall, the proposed algorithm provided significant benefits in intelligibility in anechoic and mildly reverberant environments making it suitable for hearing aid and cochlear implant applications.

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Section: B Proposed Speech Enchantment Algorithm and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dual-Microphone Algorithm That Can Cope With Competing-Talker Scenarios

Yousefian

Loizou

2013

IEEE Trans. Audio Speech Lang. Process.

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“…For notational convenience, we denote the noise PSD at microphone by . Using the expression derived in (9), the noise correlation matrix is given by (21), shown (17) ( 18) at the bottom of the page. Let and be two matrices defined as .…”

Section: B Reduced Estimation Errors With the Mvdrmentioning

confidence: 99%

“…In [21], a method was proposed that relies on the assumption that the noise field is diffuse. Two important examples of diffuse noise fields are a spherically and cylindrically isotropic noise field.…”

Section: Introductionmentioning

confidence: 99%

“…Assuming the noise field to be spherically or cylindrically isotropic, it can be shown that the noise correlation matrix is real, see, e.g., [22]. The method presented in [21] explicitly exploits this property and estimates therefore only the real part of the noise correlation matrix. Another method that exploits the assumption of a diffuse noise field is for example used in [23].…”

Section: Introductionmentioning

confidence: 99%

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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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