Localizing Spatial Information in Neural Spatiospectral Filters

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 in...

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Bibliographic Details
Published in2023 31st European Signal Processing Conference (EUSIPCO) pp. 920 - 924
Main Authors Briegleb, Annika, Haubner, Thomas, Belagiannis, Vasileios, Kellermann, Walter
Format Conference Proceeding
LanguageEnglish
Published EURASIP 04.09.2023
Subjects
Array signal processing
Artificial neural networks
Delay effects
DNN interpretability
Estimation
Europe
Logic gates
multichannel speech enhancement
spatial filtering
Speech enhancement
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Summary: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.
ISSN:2076-1465
DOI:10.23919/EUSIPCO58844.2023.10289820