Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

Zhang, J. (TU Delft Signal Processing Systems)
Heusdens, R. (TU Delft Signal Processing Systems)
Hendriks, R.C. (TU Delft Signal Processing Systems)

2019

In this paper, we present an algorithm to estimate the relative acoustic transfer function (RTF) of a target source in wireless acoustic sensor networks (WASNs). Two well-known methods to estimate the RTF are the covariance subtraction (CS) method and the covariance whitening (CW) approach, the latter based on the generalized eigenvalue decomposition. Both methods depend on the use of the noisy correlation matrix, which, in practice, has to be estimated using limited and (in WASNs) quantized data. The bit rate and the fact that we use limited data records therefore directly affect the accuracy of the estimated RTFs. Therefore, we first theoretically analyze the estimation performance of the two approaches in terms of bit rate. Second, we propose a rate-distribution method by minimizing the power usage and constraining the expected estimation error for both RTF estimators. The optimal rate distributions are found by using convex optimization techniques. The model-based methods, however, are impractical due to the dependence on the true RTFs. We therefore further develop two greedy rate-distribution methods for both approaches. Finally, numerical simulations on synthetic data and real audio recordings show the superiority of the proposed approaches in power usage compared to uniform rate allocation. We find that in order to satisfy the same RTF estimation accuracy, the rate-distributed CW methods consume much less transmission energy than the CS-based methods.

convex optimization
covariance subtraction
covariance whitening
model/data-driven rate distribution
quantization
relative transfer function
Sensor networks

http://resolver.tudelft.nl/uuid:fdff7b72-14b6-43d4-b98e-d1d239c787df

https://doi.org/10.1109/TASLP.2019.2923542

2329-9290

IEEE - ACM Transactions on Audio, Speech, and Language Processing, 27 (10), 1507-1519

Accepted author manuscript

Institutional Repository

journal article

© 2019 J. Zhang, R. Heusdens, R.C. Hendriks