Low-Complexity Data-Reuse RLS Algorithm for Stereophonic Acoustic Echo Cancellation
Source abstract
Abstract
Stereophonic audio devices employ two loudspeakers and two microphones in order to create a bidirectional sound effect. In this context, the stereophonic acoustic echo cancellation (SAEC) setup requires the estimation of four echo paths, each one corresponding to a loudspeaker-to-microphone pair. The widely linear (WL) model was proposed in recent literature in order to simplify the handling of the SAEC mathematical model. Moreover, low complexity recursive least- squares (RLS) adaptive algorithms were developed within the WL framework and successfully tested for SAEC scenarios. This paper proposes to apply a data-reuse (DR) approach for the combination between the RLS algorithm and the dichotomous coordinate descent (DCD) iterative method. The resulting WL-DR-RLS-DCD algorithm inherits the convergence properties of the RLS family and requires an amount of mathematical operations attractive for practical implementations. Simulation results show that the DR approach improves the tracking capabilities of the RLS-DCD algorithm, with an acceptable surplus in terms of computational workload.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Adaptive Filtering Techniques · Speech and Audio Processing · Blind Source Separation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Telecommunications, University Politehnica of Bucharest, 1-3, Iuliu Maniu Blvd., 061071 Bucharest, Romania
INRS-EMT, University of Quebec, 800 de la Gauchetiere Ouest, Suite 6900, Montreal, QC H5A 1K6, Canada
Funding
Ministry of Research, Innovation and Digitization
References (30)
Show source-provided references
1. Haykin, S. (2002). Adaptive Filter Theory, Prentice Hall.
2. Sayed, A. (2008). Adaptive Filters, Wiley.10.1002/9780470374122 ↗
3. Diniz, P.S.R. (2013). Adaptive Filtering: Algorithms and Practical Implementation, Springer. [4th ed.].10.1007/978-1-4614-4106-9 ↗
4. Benesty, J., Paleologu, C., Gaensler, T., and Ciochina, S. (2011). A Perspective on Stereophonic Acoustic Echo Cancellation, Springer.10.1007/978-3-642-22574-1 ↗
5. A widely linear model for stereophonic acoustic echo cancellation10.1016/j.sigpro.2012.08.017 ↗
6. Hänsler, E., and Schmidt, G. (2004). Acoustic Echo and Noise Control–A Practical Approach, Wiley.10.1002/0471678406 ↗
7. Acoustic echo control10.1016/B978-0-12-396501-1.00030-3 ↗
8. Stereophonic acoustic echo cancellation-an overview of the fundamental problem10.1109/97.404129 ↗
9. A better understanding and an improved solution to the specific problems of stereophonic acoustic echo cancellation10.1109/89.661474 ↗
10. Double-Talk Robust Multichannel Acoustic Echo Cancellation Using Least-Squares MIMO Adaptive Filtering: Transversal, Array, and Lattice Forms10.1109/TSP.2020.3011572 ↗
11. Stereophonic Acoustic Echo Suppression for Speech Interfaces for Intelligent TV Applications10.1109/TCE.2018.2843293 ↗
12. Stereo Acoustic Echo Cancellation Based on Maximum Likelihood Estimation with Inter-Channel-Correlated Echo Compensation10.1109/TSP.2020.3021240 ↗
13. Neural Cascade Architecture for Multi-Channel Acoustic Echo Suppression10.1109/TASLP.2022.3192104 ↗
14. A Mixed Decorrelation Approach for Stereo Acoustic Echo Cancellation Based on the Estimation of the Fundamental Frequency10.1109/TASL.2011.2164529 ↗
15. Schneider, M., Huemmer, C., and Kellermann, W. (2013, January 26–31). Wave-domain loudspeaker signal decorrelation for system identification in multichannel audio reproduction scenarios. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, USA.10.1109/ICASSP.2013.6637719 ↗
16. Romoli, L., Cecchi, S., and Piazza, F. (2014, January 4–9). A novel decorrelation approach for multichannel system identification. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.10.1109/ICASSP.2014.6854887 ↗
17. Multichannel Acoustic Echo Cancellation in the Wave Domain With Increased Robustness to Nonuniqueness10.1109/TASLP.2016.2515502 ↗
18. Stanciu, C., Benesty, J., Paleologu, C., Gaensler, T., and Ciochină, S. (2012, January 25–30). A novel perspective on stereophonic acoustic echo cancellation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.10.1109/ICASSP.2012.6287808 ↗
19. Stanciu, C., Paleologu, C., Benesty, J., Gaensler, T., and Ciochina, S. (2012, January 19–22). An efficient RLS algorithm for stereophonic acoustic echo cancellation with the widely linear model. Proceedings of the 41st International Congress and Exposition on Noise Control Engineering, INTERNOISE, New York, NY, USA.10.1109/ICASSP.2012.6287808 ↗
20. Acoustic echo control. an application of very-high-order adaptive filters10.1109/79.774933 ↗
21. Low-Complexity RLS Algorithms Using Dichotomous Coordinate Descent Iterations10.1109/TSP.2008.917874 ↗
22. DCD-RLS Adaptive Filters with Penalties for Sparse Identification10.1109/TSP.2013.2258340 ↗
23. Data-Reuse Recursive Least-Squares Algorithms10.1109/LSP.2022.3153207 ↗
24. Fîciu, I.D., Stanciu, C.L., Elisei-Iliescu, C., Anghel, C., and Udrea, R.M. (2022, January 13–15). Low-Complexity Implementation of a Data-Reuse RLS Algorithm. Proceedings of the 2022 45th International Conference on Telecommunications and Signal Processing (TSP), Virtual.10.1109/TSP55681.2022.9851293 ↗
25. (2022, November 05). The Open Speech Repository. Available online: https://www.itu.int/rec/T-REC-G.168/en.
26. A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification10.1109/LSP.2008.2001559 ↗
27. Stanciu, C., Paleologu, C., Benesty, J., Ciochina, S., and Albu, F. (2012, January 27–31). Variable-forgetting factor RLS for stereophonic acoustic echo cancellation with widely linear model. Proceedings of the 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), Bucharest, Romania.10.1109/ICASSP.2012.6287808 ↗
28. Linear System Identification Based on a Kronecker Product Decomposition10.1109/TASLP.2018.2842146 ↗
29. Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems10.1109/TASLP.2019.2903276 ↗
30. Identification of Room Acoustic Impulse Responses via Kronecker Product Decompositions10.1109/TASLP.2022.3202128 ↗