• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.91-98
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• 2011

In this paper, a multi-channel speech enhancement method using the linearly constrained minimum variance (LCMV) algorithm and a variable learning rate control is proposed. To control the learning rate for adaptive filters of the LCMV algorithm, the direction of arrival (DOA) is measured for each short-time input signal and the likelihood function of the target speech presence is estimated to control the filter learning rate. Using the likelihood measure, the learning rate is increased during the pure noise interval and decreased during the target speech interval. To optimize the parameter of the mapping function between the likelihood value and the corresponding learning rate, an exhaustive search is performed using the Bark's scale distortion (BSD) as the performance index. Experimental results show that the proposed algorithm outperforms the conventional LCMV with fixed learning rate in the BSD by around 1.5 dB.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.32-40
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• 2013

In this paper, we propose a multi-channel speech enhancement algorithm using beamforming and direction-of-arrival (DOA)-based masking. The proposed algorithm enhances noisy speech basically by the linearly constrained minimum variance (LCMV) algorithm and then a mel-scale Wiener filter designed using DOA-based masking is applied to remove still remaining noises. To improve the performance, we optimize the learning rate of the adaptive filters in LCMV and the DOA threshold to detect target speech spectrum. As performance indices, the perceptual evaluation of speech quality (PESQ) score and output SNRs are measured. Experimantal results show that the proposed algorithm outperforms the conventional LCMV beamformer by 0.09 in PESQ score and 5.75 dB in output SNR, respectively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.101-107
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• 2011

The optimum weight vector is studied to remove interference and jamming signals in adaptive array antenna system. The optimum weight vector is calculated to apply a minimum variance algorithm and cost function in linearly constrained conditions, and accurately estimates target's signal. Adaptive array antenna system is the system which improves signal to noise ratio(SNR) and decreases interference and jammer power. Adaptive array antenna system delays at tap output of antenna array element. Each tap finally makes the complex signal of one in multiplier complex weight. In order to obtain optimum's weight calculation, optimum weight vector is used in this paper. After simulation, resolution is increased below $3^{\circ}$, and sidelobe is decreased about 10 dB.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.3
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• pp.317-325
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• 2015

In an interference environment, adaptive sidelobe blanking(adaptive SLB: ASB) algorithm effectively cancels the high-duty cycle jammer and blocks the sidelobe signals without the auxiliary antenna. The adaptive SLB for the linearly constrained minimum variance (LCMV) is proposed in this paper. In the proposed scheme, the interference covariance matrix is modified to satisfy the direction constraints of LCMV and the normalized output can be obtained to block sidelobe signals. As the LCMV can be represented as a generalized sidelobe canceller(GSC) form, which is the general framework of various adaptive beamforming(ABF) algorithms, the proposed adaptive SLB can be applied to various ABF methods. The performance of the proposed method is verified through simulation and analysis.

• The Journal of the Acoustical Society of Korea
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• v.14 no.4
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• pp.96-99
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• 1995

Under signal range mismatch, the LCMV beamformer has the performance degradation to cancel a desired signal. Using the eigenstructure properties of the array covariance matrix, we investigate the cause of this problem. From this investigation, a robust beamformer to source range mismatch is presented. The proposed beamformer has the maximum output signal-to-noise ration (SNR). When a desired signal is in a far field, the weight vector of the proposed beamformer is not biased.

• MALSORI
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• no.66
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• pp.73-86
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• 2008

Beamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamformning, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Bark's scale spectral distortion (BSD).

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.177-184
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• 2020

In this paper, we studied to direction of arrival (DoA) estimation to use DoA and optimum weight algorithms in coherent interference channels. The DoA algorithm have been considerable attention in signal processing with coherent signals and a limited number of snapshots in a noise and an interference environment. This paper is a proposed method for the desired signal estimation using MUSIC algorithm and adaptive beamforming to compare classical subspace techniques. Also, the proposed method is combined the updated weight value with LCMV beamforming algorithm in adaptive antenna array system for direction of arrival estimation of desired signal. The proposed algorithm can be used with combination to MUSIC algorithm, linearly constrained minimum variance beamforming (LCMV) and the weight value method to accurately desired signal estimation. Through simulation, we compare the proposed method with classical direction of in order to desired signals estimation. We show that the propose method has achieved good resolution performance better that classical direction arrival estimation algorithm. The simulation results show the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.446-452
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• 2004

Under the condition of a common narrowband target signal and interference signals from several directions, the linearly constrained minimum variance (LCMV) method using the generalized sidelobe canceller (GSC) for adaptive beamforming has been exploited successfully However, in the case of wideband signals, the length of the adaptive filter must be extended. As a result, the complexity of the beamformer increases, which makes real-time implementation difficult. In this paper, we improve the convergence characteristics of the adaptive filter using the transform domain normalized least mean square (NLMS) approach based on the subband GSC structure without the increase of complexity. Besides, the M-MAX algorithm, which is one of various selective coefficient updating methods, is employed in order to remarkably reduce the computational cost without decreasing the convergence quality. With the combination of these methods, we propose a computationally efficient wideband adaptive beamformer and verify its efficiency through a series of simulations.

• The Journal of the Acoustical Society of Korea
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• v.23 no.8
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• pp.570-575
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• 2004

In the underwater acoustic systems. we can receive signals and retrieve information about a target by using a beamforming method. The most important thing in the beamforming is finding the way to optimize the mainlobe beamwidth and the sidelobe level to the desired value. One of the prominent results of beamforming method. which has been studied. is Philip's weighting function method(1) . Philip's method adaptively adjusts its weights of array to meet the desired mainlobe beamwidth and sidelobe level. It is very similar to the design method in adaptive filter. However. this method cannot easily bring us to the desired sidelobe level due to complementary relation between mainlobe beamwidth and sidelobe level. In this paper, we propose a new algorithm using partial constrained adaptation. This method makes us circumvent the above problem and meet the specification of design easily. The proposed algorithm presents a Pattern synthesis that designer can easily control the mainlobe beamwidth and the sidelobe level to the desired value while calculation time to converge is decreasing.