Differential beamformers exhibit effective performance in broadband applications, such as acoustic applications, but they have limited white noise gain. To address this limitation, this paper introduces an adaptive weighting-based algorithm designed to enhance the white
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Differential beamformers exhibit effective performance in broadband applications, such as acoustic applications, but they have limited white noise gain. To address this limitation, this paper introduces an adaptive weighting-based algorithm designed to enhance the white noise gain of the differential beamformer by leveraging the minimum variance distortionless response (MVDR) beamforming technique. For this purpose, differential beamforming is implemented in two stages: in the first stage, the spatial difference of observations is obtained, and in the second stage, the beamformer is optimized. Subsequently, by calculating the coefficients and combining the differential and MVDR beamformers, the proposed adaptive beamformer is derived. In this beamformer, to construct the output signal, the contribution of the differential and MVDR methods is dynamically adjusted using an adaptive combination coefficient, which is a function of frequency, microphone inter-distance, target angle, and the number of microphones. The proposed beamformer, considering four microphones spaced 2 cm apart reveals a remarkable enhancement in white noise gain by 35 dB and SNR gain by 18 dB at a frequency of 1 kHz. Additionally, the proposed adaptive algorithm demonstrates a 3.5 dB improvement in directivity factor over its differential counterpart.
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