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Invariance to microphone array configuration is a rare attribute in neuralbeamformers. Filter-and-sum (FS) methods in this class define the target signalwith respect to a reference channel. However, this not only complicatesformulation in reverberant conditions but also the network, which must have amechanism to infer what the reference channel is. To address these issues, thisstudy presents Delay Filter-and-Sum Network (DFSNet), a steerable neuralbeamformer invariant to microphone number and array geometry for causal speechenhancement. In DFSNet, acquired signals are first steered toward the speechsource direction prior to the FS operation, which simplifies the task into theestimation of delay-and-summed reverberant clean speech. The proposed model isdesigned to incur low latency, distortion, and memory and computational burden,giving rise to high potential in hearing aid applications. Simulation resultsreveal comparable performance to noncausal state-of-the-art.

DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement