The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2024 | Volume :02 | Issue : 02 | Page : 1-10
By
vector

Preeti Manker,

vector

Dr. Poonam Sinha,

vector

Priyesh Jaiswal,

  1. M. Tech scholar,, Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal,, Madhya Pradesh,, India
  2. HOD,, Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal,, Madhya Pradesh,, India
  3. Assistant Professor,, Department of Digital Communications, Barkatullah University Institute of Technology, Bhopal,, Madhya Pradesh,, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_121383’);});Edit Abstract & Keyword

The novel approach that this work discusses is a DCD-based iterative learning filter approach improved with deep learning methodologies, designed to improve the efficiency of acoustic echo cancellation. The proposed system can really manage both linear and nonlinear echo scenarios, dynamically adapting to fluctuating acoustic environments. The above comparative evaluations with standard filters, the standard RLS filter, indicate that the mean square error, and the standard deviation of the and the correlation coefficients show the performance of the proposed method is better. The filter delivers robust echo cancellation with reduced computational complexity and thus is best suited for real-time applications such as hands-free devices, teleconferencing, and VoIP systems. This work sets up a new standard in AEC, showing the efficacy of adaptive filtering integration with machine learning towards the support of modern communication technologies development.

Keywords: Acoustic echo cancellation (AEC), DCD-based iterative filter, deep learning, adaptive filtering, nonlinear echo, Mean Square Error (MSE), real-time communication, VoIP, teleconferencing.

[This article belongs to International Journal of Radio Frequency Innovations (ijrfi)]

How to cite this article:
Preeti Manker, Dr. Poonam Sinha, Priyesh Jaiswal. The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments. International Journal of Radio Frequency Innovations. 2024; 02(02):1-10.
How to cite this URL:
Preeti Manker, Dr. Poonam Sinha, Priyesh Jaiswal. The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments. International Journal of Radio Frequency Innovations. 2024; 02(02):1-10. Available from: https://journals.stmjournals.com/ijrfi/article=2024/view=0

Full Text PDF

Full Text PDF

References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_121383’);});Edit

  1. Venkatesh, A., Kumar, N., & Sathisha, S. B. (2024). Collaborative Edge Computing In IOT: Audio Signal Processing With Low-Power VLSI Implementation. Library Progress International, 44(3), 19270-19282.
  2. Weng, C., Gu, X., & Jin, H. (2024). Coded Excitation for Ultrasonic Testing: A Review. Sensors, 24(7), 2167. https://doi.org/10.3390/s24072167
  3. Folgado, F. J., Calderón, D., González, I., & Calderón, A. J. (2024). Review of Industry 4.0 from the perspective of automation and supervision systems: Definitions, architectures and recent trends. Electronics, 13(4), 782. https://doi.org/10.3390/electronics13040782
  4. Chaccour, C., Saad, W., Debbah, M., Han, Z., & Poor, H. V. (2024). Less data, more knowledge: Building next generation semantic communication networks. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2024.3412852
  5. Getu, T. M., Kaddoum, G., & Bennis, M. (2024). A survey on goal-oriented semantic communication: Techniques, challenges, and future directions. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3381967
  6. Mienye, I. D., Swart, T. G., & Obaido, G. (2024). Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 15(9), 517. https://doi.org/10.3390/info15090517
  7. Hojnik, J., Ruzzier, M., Ruzzier, M. K., Sučić, B., Soltwisch, B., & Rus, M. (2024). Review of EU projects with a focus on environmental quality: innovation, eco-innovation, and circular-economy elements. International Journal of Innovation Studies, 8(1), 1-12. https://doi.org/10.1016/j.ijis.2023.10.001
  8. Mahmoudi, A., Jemielniak, D., & Ciechanowski, L. (2024). Echo Chambers in Online Social Networks: a Systematic Literature Review. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3353054
  9. Orikpete, O. F., Dennis, N. M., Kikanme, K. N., & Ewim, D. R. E. (2024). Advancing noise management in aviation: Strategic approaches for preventing noise-induced hearing loss. Journal of Environmental Management, 363, 121413. https://doi.org/10.1016/j.jenvman.2024.121413
  10. Ziegler, L., & Soutullo, A. (2024). Anthropogenic noise in terrestrial Antarctica: a short review of background information, challenges and opportunities. Polar Research, 43. https://doi.org/10.33265/polar.v43.9859
  11. Pandey, P., Farach-Colton, M., Dayan, N., & Zhang, H. (2024, June). Beyond Bloom: A Tutorial on Future Feature-Rich Filters. In SIGMOD Conference Companion(pp. 636-644).
  12. Liu, Y., & Lei, Z. (2024). Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms. Applied Sciences, 14(3), 1218. https://doi.org/10.3390/app14031218
  13. Motiei, M., Iyer-Raniga, U., Andamon, M. M., & Khodabakhshian, A. (2024). Advancing Circular Buildings: A Review of Building Strategies for AEC Stakeholders. Buildings, 14(9), 2594. https://doi.org/10.3390/buildings14092594
  14. Agbobli, D., & Mo, Y. (2024). Leveraging XR in AEC education focusing on occupant behavior-related building energy use: A systematic review. Energy and Buildings, 114821. https://doi.org/10.1016/j.enbuild.2024.114821
  15. Alshahrani, M. H., & Maashi, M. S. (2024). A Systematic Literature Review: Facial Expression and Lip Movement Synchronization of an Audio Track. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3404056
  16. Velmurugan, S. (2023). A literature review on noise cancellation techniques in audiometry testing. IJSA, 4(2), 19-22.
  17. Kim, H., Hwang, S., Kim, J., & Lee, Z. (2022). Toward Smart Communication Components: Recent Advances in Human and AI Speaker Interaction. Electronics, 11(10), 1533. https://doi.org/10.3390/electronics11101533
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 18/11/2024
Accepted 22/11/2024
Published 25/11/2024
239