神经空间谱滤波器中的空间信息定位
Localizing Spatial Information in Neural Spatiospectral Filters
AI总结：
本文以COSPA为例研究神经空间谱滤波器如何表示空间信息，发现其空间信息由同时访问所有通道的GRU层特征表示，且仅依赖于波达方向。
摘要：
多通道语音增强的波束成形依赖于对声学场景空间特征的估计。在最简单的形式中，延迟求和波束成形器（DSB）对所有通道引入时间延迟，以对齐期望信号分量从而实现相干叠加。近期关于神经空间谱滤波的研究表明，这些滤波器可以用类似于传统波束成形器的波束模式来表征，这说明人工神经网络能够学习并显式表示空间结构。使用复值空间自编码器（COSPA）作为多通道语音增强的神经空间谱滤波器示例，我们研究了此类网络在何处以及如何表示空间信息。我们通过聚类表明，对于COSPA，空间信息由门控循环单元（GRU）层生成的特征表示，该层可同时访问所有通道，且这些特征不依赖于声源，而仅依赖于波达方向。
英文摘要：
Beamforming for multichannel speech enhancement relies on the estimation of spatial characteristics of the acoustic scene. In its simplest form, the delay-and-sum beamformer (DSB) introduces a time delay to all channels to align the desired signal components for constructive superposition. Recent investigations of neural spatiospectral filtering revealed that these filters can be characterized by a beampattern similar to one of traditional beamformers, which shows that artificial neural networks can learn and explicitly represent spatial structure. Using the Complex-valued Spatial Autoencoder (COSPA) as an exemplary neural spatiospectral filter for multichannel speech enhancement, we investigate where and how such networks represent spatial information. We show via clustering that for COSPA the spatial information is represented by the features generated by a gated recurrent unit (GRU) layer that has access to all channels simultaneously and that these features are not source -- but only direction of arrival-dependent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
