Beamforming has been extensively investigated for multi-channel audio
processing tasks. Recently, learning-based beamforming methods, sometimes
called \textit{neural beamformers}, have achieved significant improvements in
both signal quality (e.g. signal-to-noise ratio (SNR)) and speech recognition
(e.g. word error rate (WER)). Such systems are generally non-causal and require
a large context for robust estimation of inter-channel features, which is
impractical in applications requiring low-latency responses. In this paper, we
propose filter-and-sum network (FaSNet), a time-domain, filter-based
beamforming approach suitable for low-latency scenarios. FaSNet has a two-stage
system design that first learns frame-level time-domain adaptive beamforming
filters for a selected reference channel, and then calculate the filters for
all remaining channels. The filtered outputs at all channels are summed to
generate the final output. Experiments show that despite its small model size,
FaSNet is able to outperform several traditional oracle beamformers with
respect to scale-invariant signal-to-noise ratio (SI-SNR) in reverberant speech
enhancement and separation tasks. Moreover, when trained with a
frequency-domain objective function on the CHiME-3 dataset, FaSNet achieves
14.3\% relative word error rate reduction (RWERR) compared with the baseline
model. These results show the efficacy of FaSNet particularly in reverberant
and noisy signal conditions.

提出了一种适用于低延迟场景的时域、基于过滤的波束成形方法 ——FaSNet，该方法可在含噪声和混响语音增强中表现出色，同时还可在 CHiME-3 数据集上通过训练基于频域目标函数的 FaSNet 模型实现与基准模型相比的相对字错误率降低 14.3%。