Open Access

ARTICLE

Noise Reduction in Industry Based on Virtual Instrumentation

Radek Martinek¹, Rene Jaros¹, Jan Baros¹, Lukas Danys¹, Aleksandra Kawala-Sterniuk², Jan Nedoma^3,*, Zdenek Machacek¹, Jiri Koziorek¹

1 Faculty of Electrical Engineering and Computer Science, Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 708 00, Ostrava-Poruba, Czechia
2 Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
3 Faculty of Electrical Engineering and Computer Science, Department of Telecommunications, VSB–Technical University of Ostrava, 708 00, Ostrava-Poruba, Czechia

* Corresponding Author: Jan Nedoma. Email:

Computers, Materials & Continua 2021, 69(1), 1073-1096. https://doi.org/10.32604/cmc.2021.017568

Received 03 February 2021; Accepted 13 April 2021; Issue published 04 June 2021

Abstract

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction. In the Industry 4.0 era, the mass development of voice control (speech recognition) in various industrial applications is possible, especially as related to augmented reality (such as hands-free control via voice commands). As Industry 4.0 relies heavily on radiofrequency technologies, some brief insight into this problem is provided, including the Internet of things (IoT) and 5G deployment. This study was carried out in cooperation with the industrial partner Brose CZ spol. s.r.o., where sound recordings were made to produce a dataset. The experimental environment comprised three workplaces with background noise above 100 dB, consisting of a laser/magnetic welder and a press. A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft. We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency. Using virtual devices, the study was carried out on large speakers with 20 participants (10 men and 10 women). The experiments included a large number of repetitions (100 times for each command under different noise conditions). Statistical results confirmed the efficiency of the tested algorithms. Laser welding environment efficiency was 27% before applied filtering, 76% using the least mean square (LMS) algorithm, and 79% using LMS + independent component analysis (ICA). Magnetic welding environment efficiency was 24% before applied filtering, 70% with LMS, and 75% with LMS + ICA. Press workplace environment efficiency showed no success before applied filtering, was 52% with LMS, and was 54% with LMS + ICA.

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Keywords

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