Differential microphone arrays (DMAs) have garnered significant attention in recent research and development due to their high directivity and frequency-invariant beampatterns. However, DMAs frequently encounter substantial white noise amplification, which limits their practical applications. This paper addresses this issue by introducing a general method for designing robust DMAs with microphone arrays of arbitrary planar topology. The proposed approach approximates the beampattern using the Jacobi-Anger series expansion and constrains the white noise gain (WNG) to a specified value. This minimizes the error between the beampattern and the ideal directivity pattern while ensuring a reasonable level of robustness. A closed-form solution for the robust differential beamformer filter is derived using the quadratic eigenvalue problem (QEP) method. Simulation results demonstrate the feasibility and effectiveness of the proposed approach.

ABSTRACT Visual object tracking is widely applied in intelligent transportation systems and visual surveillance systems that serve smart cities, as well as in autonomous vehicles. Existing methods usually utilise a relation‐modelling framework to model the visual object tracking problem, with auxiliary spatial context and temporal information. The spatial context is often extracted by enlarging the target template, which can introduce more background and positional information. The temporal correlation is obtained by associating the search image with previous images. However, due to noise interference, existing methods often partially exploit auxiliary data, leading to underutilisation of spatiotemporal information. To address these issues, we propose a novel and concise tracking framework, uniformly encoding all auxiliary data, including the enlarged target template, previous images, and corresponding target bounding boxes. Specifically, to mitigate the unstable factors introduced by these raw inputs, we propose a spatiotemporal context adaptive encoder, which can adaptively select appropriate information in noisy data. Extensive experiments show that the proposed method achieves state‐of‐the‐art performance on various benchmarks, demonstrating its superiority.

Differential microphone arrays are widely used for far-field sound acquisition due to their high directivity and compact geometry. However, they lack the flexibility to adapt in dynamic acoustic environments with multiple or moving interferers. This paper proposes a novel method for fusing multiple differential beamformers to improve robustness under such conditions. A set of beamformers is designed with distortionless constraints in the target direction and nulls in various potential interference directions. An online fusion strategy is then applied, where a subset of beamformer outputs is selected and adaptively combined at each time frame based on the criterion of minimizing the instantaneous output variance. Simulation results demonstrate that the proposed method achieves superior interference suppression and speech quality, while maintaining low computational complexity suitable for real-time processing.

This paper presents an underwater acoustic target recognition method to reduce recognition errors in continuous recordings caused by variations in ship operating conditions. The proposed method comprises two-stages: the spectral feature classification and the supervised contrastive learning, and it is called as SFC-Sup as a result in this paper. In the first stage, a new spectral feature classification strategy is designed to choose appropriate feature sets for contrastive learning, based on which an instance discrimination pretext task is created by utilizing different spectral features to capture invariant features across segments under different operating conditions. In the second stage, a dynamic weighted loss function is introduced to guide the joint optimization process in the framework of contrastive learning. Different to existing methods which focus on improving the recognition accuracy by designing features for individual segments, the proposed two-stage method SFC-Sup considers consistent features across diverse segments, which is expected to improve recognition accuracy in a continuous recording. Experimental results demonstrate that in the presence of complex operating conditions, SFC-Sup exhibits superior stability and enhances recognition accuracy by 2.06% compared to state-of-the-art methods.