A Simple Theory and New Method of Differential Beamforming with Uniform Linear Microphone Arrays

Huang G, Benesty J, Cohen I, Chen J (2020)


Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 28

Pages Range: 1079-1093

Article Number: 9037110

DOI: 10.1109/TASLP.2020.2980989

Abstract

This article presents a theoretical study of differential beamforming with uniform linear arrays. By defining a forward spatial difference operator, any order of the spatial difference of the observed signals can be represented as a product of a difference operator matrix and the microphone array observations. Consequently, differential beamforming is implemented in two stages, where the first one obtains spatial difference of the observations and the second stage optimizes the beamformer. The major contributions of this article are as follows. First, we propose a new theory of differential beamforming with uniform linear arrays, which shows clearly the connection between the conventional differential beamforming and the null-constrained differential beamforming methods. This provides some new insight into the design of differential beamformers. Second, we deduce some new differential beamformers, where conventional beamforming may be seen as a particular case. Specifically, we derive the maximum white noise gain (MWNG), maximum directivity factor (MDF), parameterized MDF, and parameterized maximum front-to-back ratio differential beamformers. Third, we further extend the idea of how to design optimal differential beamformers by combining both the observed signals and their spatial differences.

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APA:

Huang, G., Benesty, J., Cohen, I., & Chen, J. (2020). A Simple Theory and New Method of Differential Beamforming with Uniform Linear Microphone Arrays. IEEE/ACM Transactions on Audio, Speech and Language Processing, 28, 1079-1093. https://doi.org/10.1109/TASLP.2020.2980989

MLA:

Huang, Gongping, et al. "A Simple Theory and New Method of Differential Beamforming with Uniform Linear Microphone Arrays." IEEE/ACM Transactions on Audio, Speech and Language Processing 28 (2020): 1079-1093.

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