A comparison of two LMS constrained optimal array structures

Jim, C.

doi:10.1109/proc.1977.10820

Cited by 74 publications

(35 citation statements)

References 2 publications

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“…The GSC is implemented based on an adaptive LMS algorithm. Comparing the GSC to the aforementioned MVDR-based methods is justified by the fact that the analytic expression of the GSC can be shown to equal the MVDR beamformer under the conditions used in this work [31].…”

Section: ) Proposed Methods and Reduction Of Estimation Errorsmentioning

confidence: 99%

“…In a similar way as the numerator of (20) was rewritten in (26)- (28) we can write the denominator as (31) By substitution of the results from (28) and (31) into (20) we then obtain for the MVDR filter-coefficients (32) The MVDR filter-coefficients have now become a function of matrix only, and are therefore independent of the noise PSD . Thus, estimation of using a single-channel noise PSD estimator like [10] or [15] is in general not necessary, and, noise PSD estimation errors will have no influence.…”

Section: B Reduced Estimation Errors With the Mvdrmentioning

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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Section: ) Proposed Methods and Reduction Of Estimation Errorsmentioning

confidence: 99%

Section: B Reduced Estimation Errors With the Mvdrmentioning

confidence: 99%

Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement

Hendriks

Gerkmann

2012

IEEE Trans. Audio Speech Lang. Process.

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“…Mathematically, this condition is expressed as follows: (19) where denotes the greatest common divisor of the polynomials involved and, and are the -transforms of and , respectively. From (14), we can deduce that a necessary condition for to be invertible is to have . When , i.e., the number of sources is equal to the number of microphones, this condition is always true, which means that there is no upper bound for .…”

Section: ) Case 1: Partial Knowledge Of the Impulse Response Matrixmentioning

confidence: 99%

“…3. This filter-and-sum algorithm was originally proposed by Frost [13] in the early 1970s and has been intensively studied since then [1], [14]- [32]. In addition to producing a broadband directivity pattern, the effectiveness of this beamformer can also be explained in the following way.…”

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confidence: 99%

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On Microphone-Array Beamforming From a MIMO Acoustic Signal Processing Perspective

Benesty

Chen²,

Huang³

et al. 2007

IEEE Trans. Audio Speech Lang. Process.

131

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Abstract-Although many microphone-array beamforming algorithms have been developed over the past few decades, most such algorithms so far can only offer limited performance in practical acoustic environments. The reason behind this has not been fully understood and further research on this matter is indispensable. In this paper, we treat a microphone array as a multiple-input multiple-output (MIMO) system and study its signal-enhancement performance. Our major contribution is fourfold. First, we develop a general framework for analyzing performance of beamforming algorithms based on the acoustic MIMO channel impulse responses. Second, we study the bounds for the length of the beamforming filter, which in turn shows the performance bounds of beamforming in terms of speech dereverberation and interference suppression. Third, we address the connection between beamforming and the multiple-input/output inverse theorem (MINT). Finally, we discuss the intrinsic relationships among different classical beamforming techniques and explain, from the channel condition perspective, what the prerequisites are for those techniques to work.Index Terms-Beamforming, Frost, linearly constrained minimum variance (LCMV), microphone arrays, multiple-input/ output inverse theorem (MINT), minimum variance distortionless response (MVDR).

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