BeamformNet: Deep Learning-Based Beamforming Method for DoA Estimation via Implicit Spatial Signal Focusing and Noise Suppression

TL;DR

BeamformNet introduces a deep learning framework that enhances traditional beamforming for DoA estimation, achieving state-of-the-art accuracy and robustness in challenging acoustic scenarios.

Contribution

It proposes a novel neural network-based beamforming method that implicitly focuses spatial signals and suppresses noise, improving DoA estimation under difficult conditions.

Findings

Achieves state-of-the-art DoA estimation accuracy.

Demonstrates robustness in simulated and real-world data.

Outperforms traditional beamforming methods in challenging scenarios.

Abstract

Deep learning-based direction-of-arrival (DoA) estimation has gained increasing popularity. A popular family of DoA estimation algorithms is beamforming methods, which operate by constructing a spatial filter that is applied to array signals. However, these spatial filters obtained by traditional model-driven beamforming algorithms fail under demanding conditions such as coherent sources and a small number of snapshots. In order to obtain a robust spatial filter, this paper proposes BeamformNet-a novel deep learning framework grounded in beamforming principles. Based on the concept of optimal spatial filters, BeamformNet leverages neural networks to approximately obtain the optimal spatial filter via implicit spatial signal focusing and noise suppression, which is then applied to received signals for spatial focusing and noise suppression, thereby enabling accurate DoA estimation. Experimental results on both simulated and real-world speech acoustic source localization data demonstrate that BeamformNet achieves state-of-the-art DoA estimation performance and has better robustness.