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This dissertation focuses on the investigation and evaluation of adptivealgorithms for multichannel active noise control system. The aim of theresearch is to investigate the effectiveness of the FxLMS algorithm and thepre-trained control filter in attenuating various types of noise. The studybegins with a comprehensive review of the existing literature on active noisecontrol, highlighting the significance of noise reduction in differentapplications. The theoretical foundations of the FxLMS algorithm and thepre-trained control filter are then presented, including their underlyingprinciples and mathematical formulations. Through a comprehensive analysis, aclear understanding of these methods is established. To assess the performanceof the FxLMS algorithm and the pre-trained control filter, extensive simulationexperiments are conducted using real-world noise signals. The experimentsinclude scenarios such as aircraft noise, traffic noise, and mixed noise. Theresults of the simulations are used to compare the noise reduction capabilitiesof the two methods and to evaluate their effectiveness under different noiseconditions. The findings indicate that the FxLMS algorithm exhibits remarkablenoise reduction performance. It demonstrates a strong ability to track andrespond quickly to varying noise patterns. In the initial stages of noisereduction, the pre-trained control filter shows better performance. However, astime progresses, the FxLMS algorithm consistently achieves higher average noisereduction levels compared to the pre-trained control filter. Additionally, theFxLMS algorithm shows faster responsiveness during transitional periods whennoise characteristics change. These findings contribute to the field of noisecontrol and provide valuable insights for designing efficient noise reductionsystems.

Investigation and Evaluation of Adaptive Algorithms for Multichannel Active Noise Control System