Neural Network Adaptive Beamforming for Robust Multichannel Speech Recognition

2016 ◽  
Author(s):  
Bo Li ◽  
Tara N. Sainath ◽  
Ron J. Weiss ◽  
Kevin W. Wilson ◽  
Michiel Bacchiani
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Adaptive Beamforming

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

2021 ◽  
pp. 1-7
Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phased Array ◽  
Weight Vector ◽  
Adaptive Beamforming ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Array Antennas ◽  
Optimum Weight ◽  
High Computational Complexity

Abstract Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

Sign in / Sign up

Export Citation Format

Share Document