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Correlation Maximization-based Sampling Rate Offset Estimation with Multichannel Node Information
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DOI：10.1016/j.sigpro.2026.110627.png)
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Abstract
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This paper first exploits multichannel information within each node, either by utilizing all microphone pairs or by applying beamforming outputs, to estimate the sampling rate offset (SRO) between nodes in an acoustic sensor network. The traditional correlation maximization-based method only considers one channel per node, which may limit the accuracy of the SRO estimation. To address this, we extend the correlation maximization framework by incorporating multichannel node information to enhance the performance of SRO estimation. The first strategy calculates the correlation function using all pairwise combinations of microphones between nodes. The second strategy involves first applying beamformers to the multichannel signals at each node, followed by computing the correlation function of the beamforming outputs for SRO estimation. The simulation results show that both proposed methods robustly achieve higher estimation accuracy than the traditional correlation maximization-based method in noisy and reverberant environments.
Keywords:
Sampling Rate Offset
Multichannel Information
Correlation Maximization
Acoustic Sensor Network
Beamforming
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