Work

One-Shot Distributed Node-Specific Signal Estimation with Non-Overlapping Latent Subspaces in Acoustic Sensor Networks

Year: 2024

Type: conference-paper

Abstract: A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlapping latent signal subspaces. The iDANSE algorithm matches the performance of a centralized algorithm in a single processing cycle while devices exchange fused versions of their multichannel local microphone signals. Key iterationless nature, which favors deployment in real-time applications, and the fact that devices can exchange fewer fused signals than the number of latent sources in the environment. The proposed method is validated in numerical simulations including a speech enhancement scenario.

Source: International Workshop on Acoustic Signal Enhancement

DOI: 10.1109/iwaenc61483.2024.10694548

Authors: Paul Didier, Pourya Behmandpoor, Toon van Waterschoot, Marc Moonen

Institution: KU Leuven

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Fonds Wetenschappelijk Onderzoek, KU Leuven

Awards: G434337789, User-centric distributed signal processing algorithms for next generation cell-free massive MIMO based wireless communication networks, Service-Oriented Ubiquitous Network-Driven Sound