@Article{cmc.2022.019904, AUTHOR = {Radek Martinek, Jan Baros, Rene Jaros, Lukas Danys, Jan Nedoma}, TITLE = {Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks}, JOURNAL = {Computers, Materials \& Continua}, VOLUME = {71}, YEAR = {2022}, NUMBER = {3}, PAGES = {4659--4676}, URL = {http://www.techscience.com/cmc/v71n3/46450}, ISSN = {1546-2226}, ABSTRACT = {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%.}, DOI = {10.32604/cmc.2022.019904} }