Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method t...

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Bibliographic Details
Published in:IEEE open journal of signal processing Vol. 4; pp. 1 - 9
Main Authors: Didier, Paul, Van Waterschoot, Toon, Doclo, Simon, Moonem, Marc
Format: Journal Article
Language:English
Published: New York IEEE 01-01-2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Artificial neural networks
coherence drift
Compensation
Estimation
Microphones
Nodes
Noise reduction
Sampling
Sampling rate offsets
signal enhancement
Signal processing
Signal processing algorithms
Signal transmission
Time-domain analysis
weighted overlap-add
wireless acoustic sensor networks
Wireless communication
Wireless networks
Wireless sensor networks
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Summary:Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method to allow the deployment of the distributed adaptive node-specific signal estimation (DANSE) algorithm in WASNs composed of asynchronous devices. The signals available at each node are first utilised in a coherence-drift-based method to blindly estimate SROs which are then compensated for via phase shifts in the frequency domain. A modification of the weighted overlap-add (WOLA) implementation of DANSE is introduced to account for SRO-induced full-sample drifts, permitting per-sample signal transmission via an approximation of the WOLA process as a time-domain convolution. The performance of the proposed algorithm is evaluated in the context of distributed noise reduction for the estimation of a target speech signal in an asynchronous WASN.
ISSN:2644-1322
2644-1322
DOI:10.1109/OJSP.2023.3243851