Existing frame-wise neural beamformers for speech extraction can obtain promising performance in relatively high signal-to-noise ratio (SNR) scenarios using small microphone arrays, while they still suffer from performance degradation in relatively low SNR environments, e.g., SNR<-5 dB. As an attempt to solve this problem, this paper proposes an all-neural beamformer based on Kronecker product decomposition, denoted by NeuKP-BF, for large-scale microphone arrays. The core idea is to incorporate the high spatial resolution of large microphone arrays and the powerful non-linear modeling capability of deep neural networks to improve speech extraction performance in challenging environments. In this paper, to reduce the feature representation redundancy and improve the interpretability, we used the Kronecker product rule to decompose the original large-scale array into two small virtual subarrays, and beamformers for the two subarrays were then designed and merged finally. The whole system was designed to implement in an end-to-end manner. Experiments were conducted on both the synthesized data using the DNS-Challenge corpus. The results showed that the proposed approach outperformed existing advanced baselines in terms of multiple objective metrics.

Geometry calibration of microphone arrays is an essential preprocessing step for many applications, e.g., beamforming in deformable arrays. However, most existing geometry calibration methods involve the use of non-convex cost functions, suffering from the local minimum problem and low stability. To overcome these drawbacks, we introduce a novel approach that leverages the geometry feature of deformable linear arrays (DLAs) as an additional constraint. The proposed method employs Bézier curve fitting, utilizing the characteristics of Bézier curves to model the geometry feature. Specifically, we first introduce the general form of the geometry calibration problem, and an alternative approach is then proposed for a specific scenario where quadratic Bézier curves are used to fit the array shape. Finally, an additional scale modification is adopted to improve the performance of the proposed method in real scenarios. Simulations and real experiments validate the effectiveness of the proposed method for geometry calibration of DLAs.

Although deep learning based beamformers have achieved promising performance using small microphone arrays, they suffer from performance degradation in very challenging environments, such as extremely low Signal-to-Noise Ratio (SNR) environments, e.g., SNR$\le$−10 dB. A large-scale microphone array with dozens or hundreds of microphones can improve the performance of beamformers in these challenging scenarios because of its high spatial resolution. While a dramatic increase in the number of microphones leads to feature redundancy, causing difficulties in feature extraction and network training. As an attempt to improve the performance of deep beamformers for speech extraction in very challenging scenarios, this paper proposes a novel all neural Kronecker product beamforming denoted by ANKP-BF for large-scale microphone arrays by taking the following two aspects into account. Firstly, a larger microphone array can provide higher performance of spatial filtering when compared with a small microphone array, and deep neural networks are introduced for their powerful non-linear modeling capability in the speech extraction task. Secondly, the feature redundancy problem is solved by introducing the Kronecker product rule to decompose the original one high-dimension weight vector into the Kronecker product of two much lower-dimensional weight vectors. The proposed ANKP-BF is designed to operate in an end-to-end manner. Extensive experiments are conducted on simulated large-scale microphone-array signals using the DNS-Challenge corpus and WSJ0-SI84 corpus, and the real recordings in a semi-anechoic room and outdoor scenes are also used to evaluate and compare the performance of different methods. Quantitative results demonstrate that the proposed method outperforms existing advanced baselines in terms of multiple objective metrics, especially in very low SNR environments.