This paper investigates the design of adaptive differential beamforming using small-spacing linear microphone arrays. We express the differential beamformer as a linear function of the target beampattern coefficients through orthogonal polynomial expansions. Consequently, the design of the beamformer reduces to optimizing these coefficients. To ensure that the maximum array response consistently aligns with the look direction, we derive constraints on the target beampattern coefficients, resulting in two convex sets for the first two orders of beampatterns. This approach uncovers numerous effective beampatterns beyond traditional options such as dipole, cardioid, supercardioid, and hypercardioid. By minimizing the power of the array output while adhering to the beampattern constraints, we develop the MPPCAD beamformer. Simulation results demonstrate that the proposed beamformer significantly enhances speech quality compared to classical differential beamformers.

This paper is devoted to the problem of noise covariance matrix (NCM) estimation. It proposes a time-frequency masking based approach. We first present an optimal mask function based on the mean-squared error criterion. To estimate this mask, we employ the recently developed directional gain method based on the knowledge of the signal incident angle. To demonstrate the effectiveness of the proposed NCM estimator, we integrate it into the minimum variance distortionless response (MVDR) beamformer. The speech enhancement results in noise-plus-interference environments show the advantages of the proposed method over two baseline beamforming algorithms.

This paper is devoted to the problem of adaptive beamforming with small-spaced microphone arrays. In this context, the Wiener filter is an optimal beamformer in the mean-squared error (MSE) sense. However, it requires good estimates of the covariance matrices of the speech signal of interest and noise, which are difficult to achieve in time-varying and reverberant acoustic environments. To deal with this problem, we propose a general method by parametric modeling the covariance matrices of speech and noise, which leads to a simplified Wiener beamformer. This beamformer has only one time-varying parameter to estimate, which is much easier to achieve as compared to the estimation of covariance matrices. As an example, we adopt the parametric model used in the superdirective beamformer, which models the covariance matrices as a combination of the pseudo-coherence matrices of a point source and diffuse noise. Simulation results show that the developed beamformer outperforms the traditional Wiener beamformer in terms of both noise and reverberation suppression.

A general model for constructing minimal length checking sequences employing a distinguishing sequence is proposed. The model is based on characteristics of checking sequences and a set of state recognition sequences. Some existing methods are shown to be special cases of the proposed model and are proven to construct checking sequences. The minimality of the resulting checking sequences is discussed and a heuristic algorithm for the construction of minimal length checking sequences is given.