Online neural fusion of distortionless differential beamformers for robust speech enhancement
This work addresses the challenge of adapting beamforming to rapidly changing interference for speech enhancement applications, representing an incremental improvement over existing adaptive methods.
The paper tackled the problem of robust speech enhancement in dynamic acoustic environments by proposing a neural fusion framework for multiple distortionless differential beamformers, achieving stronger interference suppression compared to conventional adaptive convex combination methods.
Fixed beamforming is widely used in practice since it does not depend on the estimation of noise statistics and provides relatively stable performance. However, a single beamformer cannot adapt to varying acoustic conditions, which limits its interference suppression capability. To address this, adaptive convex combination (ACC) algorithms have been introduced, where the outputs of multiple fixed beamformers are linearly combined to improve robustness. Nevertheless, ACC often fails in highly non-stationary scenarios, such as rapidly moving interference, since its adaptive updates cannot reliably track rapid changes. To overcome this limitation, we propose a frame-online neural fusion framework for multiple distortionless differential beamformers, which estimates the combination weights through a neural network. Compared with conventional ACC, the proposed method adapts more effectively to dynamic acoustic environments, achieving stronger interference suppression while maintaining the distortionless constraint.