Open Access iconOpen Access



Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Radek Martinek1, Jan Baros1, Rene Jaros1, Lukas Danys1,*, Jan Nedoma2

1 VSB–Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, 708 00, Ostrava-Poruba, Czechia
2 VSB–Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Telecommunications, 708 00, Ostrava-Poruba, Czechia

* Corresponding Author: Lukas Danys. Email: email

Computers, Materials & Continua 2022, 71(3), 4659-4676.


This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction. Modern vehicles are nowadays increasingly supporting voice commands, which are one of the pillars of autonomous and SMART vehicles. Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle background noise. This article presents the new concept of a hybrid system, which is implemented as a virtual instrument. The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction. The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks, which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles. On average, the unfiltered voice commands were successfully recognized in 29.34% of all scenarios, while the LMS reached up to 71.81%, and LMS-ICA hybrid improved the performance further to 73.03%.


Cite This Article

R. Martinek, J. Baros, R. Jaros, L. Danys and J. Nedoma, "Hybrid in-vehicle background noise reduction for robust speech recognition: the possibilities of next generation 5g data networks," Computers, Materials & Continua, vol. 71, no.3, pp. 4659–4676, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1338


  • 841


  • 0


Share Link