Adaptive Beamforming for Audio Signal Acquisition

Herbordt, Wolfgang; Kellermann, Walter

doi:10.1007/978-3-662-11028-7_6

Cited by 60 publications

(10 citation statements)

References 109 publications

(135 reference statements)

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“…Applying this inequality to (13), we deduce an upper bound of the SNR gain: (15) where denotes the trace of a square matrix. We observe how the gain is upper bounded [as long as…”

Section: B Snr Gainmentioning

confidence: 99%

Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle

Pan

Chen

Benesty

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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Linear microphone arrays combined with the minimum variance distortionless response (MVDR) beamformer have been widely studied in various applications to acquire desired signals and reduce the unwanted noise. Most of the existing array systems assume that the desired sources are in the broadside direction. In this paper, we study and analyze the performance of the MVDR beamformer as a function of the source incidence angle. Using the signal-to-noise ratio (SNR) and beampattern as the criteria, we investigate its performance in four different scenarios: spatially white noise, diffuse noise, diffuse-plus-white noise, and point-source-plus-white noise. The results demonstrate that the optimal performance of the MVDR beamformer occurs when the source is in the endfire directions for diffuse noise and point-source noise while its SNR gain does not depend on the signal incidence angle in spatially white noise. This indicates that most current systems may not fully exploit the potential of the MVDR beamformer. This analysis does not only help us better understand this algorithm, but also helps us design better array systems for practical applications.Index Terms-Beamforming, diffuse noise, microphone arrays, minimum variance distortionless response (MVDR) beamformer, noise reduction, spatially white noise, speech enhancement.

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“…Applying this inequality to (13), we deduce an upper bound of the SNR gain: (15) where denotes the trace of a square matrix. We observe how the gain is upper bounded [as long as…”

Section: B Snr Gainmentioning

confidence: 99%

Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle

Pan

Chen

Benesty

2014

IEEE/ACM Trans. Audio Speech Lang. Process.

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show abstract

“…Taking all this information into account in our optimization problem subject to (22) we find the solution…”

Section: ) Case 2: Full Knowledge Of the Impulse Response Matrix Andmentioning

confidence: 99%

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

Benesty

Chen²,

Huang³

et al. 2007

IEEE Trans. Audio Speech Lang. Process.

128

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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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“…In the last decades antenna array signal processing was successfully applied to different research areas such as communications [1], [2], audio [3], [4] and radar [5]. In some cases, antenna arrays have structures that can be exploited via multidimensional techniques.…”

Section: Introductionmentioning

confidence: 99%

Generalized sidelobe cancellers for multidimensional separable arrays

Miranda

Costa

Roemer

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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The usage of antenna arrays brought innumerable benefits to radio systems in the last decades. Arrays can have multidimensional structures that can be exploited to achieve superior performance and lower complexity. However, the literature has not explored yet all the advantages arising from these features. This paper uses tensors to provide a method to design efficient beamformers for multidimensional antenna arrays. In this work, the generalized sidelobe canceller (GSC) is extended to a multidimensional array to create the proposed R-Dimensional GSC (R-D GSC). The proposed scheme has a lower computational complexity and, under certain conditions, exhibits an improved signal to interference and noise ratio (SINR)

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Adaptive Beamforming for Audio Signal Acquisition

Cited by 60 publications

References 109 publications

Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle

Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle

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

Generalized sidelobe cancellers for multidimensional separable arrays

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