This paper introduces a new bilinear algorithm that combines distortionless response beamforming with weighted prediction error using the Kronecker product operator to improve speech signal quality in different acoustic environments. The algorithm utilizes recursive least squares cost functions and handles real dynamic, noisy, and reverberant conditions effectively. Compared to a recently proposed approach, our approach outperforms separating the desired source from undesired sources while providing dereverberation and noise suppression. It is also preferable regarding the quality of the enhanced signals it produces. In contrast, it exhibits a more considerable desired signal distortion and reduced intelligibility. The algorithm’s computational efficiency and robustness make it suitable for real-time applications, as validated using real recordings from the SPeech Enhancement for Augmented Reality (SPEAR) challenge.

Microphone array geometry plays a crucial role in array signal processing and beamforming. This paper presents an array geometry optimization approach for near-field beamforming. Considering a continuous region of interest, we aim to maximize the worst-case broadband directivity of the array while ensuring sufficiently high white noise gain. We use a near-field wave propagation analysis to formulate a convex optimization problem and find the optimal linear array topology. We evaluate the performance of the proposed approach for different desired signal directions and distances and compare our results to traditional beamformers. The optimized non-uniform geometry achieves better white noise gain and higher array directivity than standard uniform linear arrays. Furthermore, the proposed method can be used for near- and far-field sources.

In this paper, we present a generalized approach for differential microphone array (DMA) beamforming in the short-time Fourier transform (STFT) domain. We propose a multistage beamforming approach, which considers a Kronecker product (KP) decomposition of the global beamformer into two independent sub-beamformers. We derive differential KP beamformers according to different criteria and analyze their performances, which are tuned by three design parameters. These parameters allow a high beamforming design flexibility; in particular, non-differential or non-KP beamformers may be obtained as special cases. Depending on the selection of parameters, we demonstrate a preferable performance with the new approach with respect to the white noise gain and directivity factor measures. In addition, we consider the task of speech enhancement. We show that differential KP beamformers perform better than non-differential and non-KP beamformers in terms of the quality and intelligibility of their respective time-domain enhanced signals, particularly in moderately reverberant environments.