Neural Optimization Of Geometry And Fixed Beamformer For Linear Microphone Arrays

Fixed beamforming based on uniform linear microphone arrays often suffers from non-optimal performance for broadband signals. This paper addresses the issue by jointly optimizing the array geometry and spatial filters through a neural network based model. The model, composed of two feed forward neural networks, is optimized in an end-to-end manner. It satisfies the distortionless constraint in the look direction. Experimental results show that the proposed model outperforms the previous state-of-the-art fixed beamformer with overall better scores. Moreover, the proposed model can control the tradeoff between Directivity Factor (DF) and White Noise Gain (WNG) in a flexible way.