Theoretical noise reduction limits of the generalized sidelobe canceller (GSC) for speech enhancement

Bitzer, Jöerg; Simmer, Klaus Uwe; Kammeyer, Karl‐Dirk

doi:10.1109/icassp.1999.761385

Cited by 76 publications

(51 citation statements)

References 14 publications

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“…To cope with the spatially incoherent noise components, a GSC based method that incorporates an adaptive Wiener filter in the look direction was proposed in (Fischer & Simmer, 1996). The authors in (Bitzer et al, 1999) have investigated the theoretical noise reduction limits of the GSC. They have shown that this structure performs well in anechoic rooms, but it does not work well in diffuse noise fields.…”

Section: State Of the Artmentioning

confidence: 99%

Real-Time Dual-Microphone Speech Enhancement

Trabelsi¹,

François-Raymond²,

Savaria³

2012

Speech Enhancement, Modeling and Recognition- Algorithms and Applications

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Section: State Of the Artmentioning

confidence: 99%

Real-Time Dual-Microphone Speech Enhancement

Trabelsi¹,

François-Raymond²,

Savaria³

2012

Speech Enhancement, Modeling and Recognition- Algorithms and Applications

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“…Adaptive beamformers are very effective in the presence of coherent interference. It has been shown in [12] that the noise reduction performance of GSC theoretically reaches infinity in a single-source coherent noise field. Sensitivity to microphone mismatch, problems in implementing the delay (i.e., usually a fractional sample delay) and degradation of the performance in reverberant environments are among the most important drawbacks of beamforming techniques [4], [13], [14].…”

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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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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“…The study showed that a tradeoff between noise reduction and dereverberation has to be made. In [13], Bitzer et al investigated the theoretical performance limits of the GSC in the case of a spatially diffuse noise. The tradeoff between noise reduction and speech distortion in the parameterized multichannel Wiener filtering was established in [7].…”

mentioning

confidence: 99%

“…The PSD of the received signal at the th microphone can be expressed as (12) where , , , and are the PSDs of the th microphone signal, the desired signal at the th microphone, the undesired signal at the th microphone and the ambient noise signal at the th microphone, respectively. The PSD of the beamformer output is given by (13) where (14) (15) and (16) is the PSD matrix of the noise field. In the rest of this paper, we assume that is a full-rank matrix such that its inverse exists.…”

mentioning

confidence: 99%

A Speech Distortion and Interference Rejection Constraint Beamformer

Habets

Benesty

Naylor

2012

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Signals captured by a set of microphones in a speech communication system are mixtures of desired and undesired signals and ambient noise. Existing beamformers can be divided into those that preserve or distort the desired signal. Beamformers that preserve the desired signal are, for example, the linearly constrained minimum variance (LCMV) beamformer that is supposed, ideally, to reject the undesired signal and reduce the ambient noise power, and the minimum variance distortionless response (MVDR) beamformer that reduces the interference-plus-noise power. The multichannel Wiener filter, on the other hand, reduces the interference-plus-noise power without preserving the desired signal. In this paper, a speech distortion and interference rejection constraint (SDIRC) beamformer is derived that minimizes the ambient noise power subject to specific constraints that allow a tradeoff between speech distortion and interference-plus-noise reduction on the one hand, and undesired signal and ambient noise reductions on the other hand. Closed-form expressions for the performance measures of the SDIRC beamformer are derived and the relations to the aforementioned beamformers are derived. The performance evaluation demonstrates the tradeoffs that can be made using the SDIRC beamformer.Index Terms-Beamforming, frequency domain, linearly constrained minimum variance (LCMV), minimum variance distortionless response (MVDR), noise reduction, parameterized multichannel Wiener filter, speech enhancement.

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Theoretical noise reduction limits of the generalized sidelobe canceller (GSC) for speech enhancement

Cited by 76 publications

References 14 publications

Real-Time Dual-Microphone Speech Enhancement

Real-Time Dual-Microphone Speech Enhancement

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

A Speech Distortion and Interference Rejection Constraint Beamformer

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