This paper presents a novel approach utilizing uniform rectangular arrays to design a constant-beamwidth (CB) linearly constrained minimum variance (LCMV) beamformer, which also improves white noise gain and directivity. By employing a generalization of the convolutional Kronecker product beamforming technique, we decompose a physical array into virtual subarrays, each tailored to achieve a specific desired feature, and we subsequently synthesize the original array’s beamformer. Through simulations, we demonstrate that the proposed approach successfully achieves the desired beamforming characteristics while maintaining favorable levels of white noise gain and directivity. A comparative analysis against existing methods from the literature reveals that the proposed method performs better than the existing methods.

This work studies beamformers designed to achieve multiple characteristics simultaneously, specifically those compromising white-noise gain and directivity factor. We compare methods explicitly designed for these joint features against those obtained by combining specific single-task beamformers. Through simulations, we demonstrate that the robust superdirective and the tunable beamformers yield the best results among those studied. Notably, these two methods produced nearly identical outputs across all evaluated metrics. These two are also more practical, continuously compromising between the two objectives.

This work presents a new proposal for modeling the effects of quantization and saturation resulting from the reduction of the communication rate in binaural hearing aids. The behavior of the multichannel Wiener filter in acoustic noise reduction applications is analyzed. Comparisons with the classic model used in the literature are presented in terms of signal-to-noise ratio, quality and intelligibility of speech processed in the hearing aid outputs. The results obtained indicate that the proposed model presents an increase in the prediction accuracy, especially in relation to the signal-to-noise ratio and speech quality.

Aparelhos auditivos biauriculares têm como objetivo a compensação de limitações na audição humana. Consistem em dois dispositivos de amplificação sonora posicionados nas orelhas, que trocam informações entre si, de forma a propiciar maior inteligibilidade e qualidade da fala. Este trabalho tem como objetivo apresentar um novo modelo matemático para a predição de desempenho do processo de redução de ruído quando a taxa de comunicação entre os dois aparelhos é diminuída para economizar energia. O modelo convencional para a quantização é generalizado para incluir também a saturação. Resultados de simulação indicam maior acurácia na capacidade de predição de desempenho.

In this paper, we examine the use of the Single-Sideband Transform (SSBT) for convolutive beamformers. We explore its unique properties and implications for beamformer design. Our study sheds light on the tradeoffs involved in using the SSBT in beamforming applications, offering insights into both its strengths and limitations. Despite the advantage of having real-valued coefficients, we show that the convolution handling of the transform presents challenges that impact fundamental beamforming principles. When compared to the Short-Time Fourier Transform (STFT), the SSBT displays lower robustness, especially in scenarios involving mismatch and modeling noise. Notably, we establish a direct equivalence between the SSBT and STFT when using identical transform parameters, enabling their seamless interchangeability and joint use in time–frequency signal enhancements. We validate our theoretical findings through realistic simulations using the Minimum-Power Distortionless Response beamformer. These simulations illustrate that although the STFT performs marginally better than the SSBT under optimal conditions, it outperforms significantly in non-ideal scenarios.