Microphone Array Processing
M. Kalantari; M. Mohammadpour Tuyserkani; S.H. Amiri
Abstract
Background and Objectives: Operating frequency range of a microphone array is limited by the array configuration. Spatial aliasing occurs at frequencies considered to be out of the microphone array operating range that leads to side-lobes in the array beam pattern and consequently degrades the performance ...
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Background and Objectives: Operating frequency range of a microphone array is limited by the array configuration. Spatial aliasing occurs at frequencies considered to be out of the microphone array operating range that leads to side-lobes in the array beam pattern and consequently degrades the performance of the microphone array. In this paper, a general approach for increasing the operational bandwidth of the spherical microphone array without physical changes to the microphone array is proposed. Methods: Recently, Alon and Rafaely proposed a beamforming method with aliasing cancellation and formulated it for some well-known beamformers such as maximum directivity (MD), maximum white noise gain (WNG), and minimum variance distortionless response (MVDR) which have been called MDAC, MGAC, MVDR-AC beamformer respectively. In this paper, we derive MDAC method from different point of view. Then, based on our perspective, we propose a new method that is easily applicable for any beamforming algorithms.Results: Comparing with MDAC and MGAC beamformers, performance measures for our approach show improvement in directivity index (DI) and white noise gain (WNG) by nearly 19% and 15% respectively.Conclusion: Aliasing and, in consequence, unwanted side lobe formation is the main factor in spherical microphone arrays operational bandwidth determination. Most of the methods previously presented to reduce aliasing demanded physical changes in the array structure which comes at a cost. In this paper we propose a new method based on Alon and Rafaely’s approach via designing a constrained optimization problem using orthogonality property of spherical harmonics, to achieve better performance.
Microphone Array Processing
M. Kalantari
Abstract
Background and Objectives: One major problem in the minimum power distortionless response (MPDR) beamformer is the signal cancellation problem, i.e., the desired signal is canceled by the reflected signal, even though the distortionless response constraint is satisfied. Solving this problem is the objective ...
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Background and Objectives: One major problem in the minimum power distortionless response (MPDR) beamformer is the signal cancellation problem, i.e., the desired signal is canceled by the reflected signal, even though the distortionless response constraint is satisfied. Solving this problem is the objective of this paper. Methods: It is well known that the signal cancellation problem can be avoided by minimizing the cross-spectrum matrix of noise, i.e., using the minimum variance distortionless response (MVDR) beamformer. But, in the case of disturbance signals which have correlation with the desired signal, estimation of this matrix is a challenging problem. In this paper we propose an approach for estimating the cross-spectrum matrix of noise signal from which we can solve the signal cancellation problem. Results: Simulation examples show that using the proposed method we can bypass the signal cancellation problem completely. Conclusion: A common belief is that in the case of a disturbance that is a reflected version of the desired signal, due to cohesive appearance and disappearance of both the disturbance and the desired signal, the estimation of cross-spectrum matrix of noise signal is typically not possible in practice. So, based on this common belief, we can’t use the MVDR beamformer in this case. In this paper, we show that this common belief is a fault. We propose a general approach for estimating the cross-spectrum matrix of noise signal that is applicable even in the case of correlated disturbances.
