Abstract
The performance of existing sampling rate offset (SRO) estimation algorithms can be degraded significantly in low signal-to-noise ratio (SNR) conditions. To address this problem, we propose the frequency-sliding double-cross correlation processing (FS-DXCP) algorithm based on the subband secondary generalized cross-correlation function to estimate SRO. The proposed algorithm adopts a frequency-domain sliding window to construct the subband SGCC function matrix of the sensor signals. Then, by utilizing the singular value decomposition (SVD), we adaptively mitigate the influence of low SNR frequency bins on estimating secondary generalized cross-correlation functions. Finally, a higher precision SRO estimation is achieved by tracking the maximum point of the estimated SGCC function. Computer simulations show that the root mean squared error of the proposed method for sampling rate offset is 4.21 ppm when the SNR is −5 dB, which is about 8.17 ppm lower than that of the double-cross correlation processing with phase transform (DXCP-PHAT) algorithm. The proposed algorithm effectively improves the estimation accuracy of the SRO in low SNR conditions.
| Translated title of the contribution | Sampling-Rate Offset Estimation for Wireless Acoustic Sensor Networks in Low SNR Environments |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 2131-2140 |
| Number of pages | 10 |
| Journal | Tien Tzu Hsueh Pao/Acta Electronica Sinica |
| Volume | 52 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Jun 25 2024 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2024 Chinese Institute of Electronics. All rights reserved.
ASJC Scopus Subject Areas
- Electrical and Electronic Engineering
Keywords
- Sampling-rate offset estimation
- Sub-band processing
- Wireless acoustic sensor networks