Attention-based distributed speech enhancement for unconstrained microphone arrays with varying number of nodes

TL;DR

This paper introduces an attention-based neural network approach for speech enhancement in ad-hoc microphone arrays that can adapt to varying numbers of devices and handle link failures effectively.

Contribution

It proposes a novel attention mechanism that dynamically weights signals from different microphones, enabling robust speech enhancement in variable and unreliable array configurations.

Findings

Effective handling of varying microphone counts.

Robustness to link failures in microphone arrays.

Improved speech quality in ad-hoc array scenarios.

Abstract

Speech enhancement promises higher efficiency in ad-hoc microphone arrays than in constrained microphone arrays thanks to the wide spatial coverage of the devices in the acoustic scene. However, speech enhancement in ad-hoc microphone arrays still raises many challenges. In particular, the algorithms should be able to handle a variable number of microphones, as some devices in the array might appear or disappear. In this paper, we propose a solution that can efficiently process the spatial information captured by the different devices of the microphone array, while being robust to a link failure. To do this, we use an attention mechanism in order to put more weight on the relevant signals sent throughout the array and to neglect the redundant or empty channels.