Print Email Facebook Twitter Predicting noise attenuation level in the earplugs using Gaussian Process Regression Title Predicting noise attenuation level in the earplugs using Gaussian Process Regression Author Rakesh Arya, Stephy Annie Curie (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Hendriks, R.C. (mentor) Martinez, Jorge (mentor) Verhoeven, C.J.M. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering Date 2022-10-10 Abstract The earplug development followed by ALPINE, a hearing protection company is a trial and error method. This method leads to high material wastage. It is also a time-consuming and expensive development process. So, the objective of this thesis is to ease the error earplug development process by implementing a prediction model. The research is steered towards understanding the factors that influence the sound dampening properties in an earplug, building the dataset, and analysing the data and regression models. The regression model must be able to predict the noise attenuation provided by an earplug based on the material and design specifications for which Gaussian Process Regression (GPR) model is used. With RBF kernel function and through hyperparameter optimization, the GPR model is trained and tested on datasets.The main finding of this thesis is that noise attenuation provided by an earplug is highly subjective. Sound perception is crucial in earplug design. Even though the most ideal sound attenuation prediction model for earplugs can be developed, in the end, everything relies on individual sound perception. So, the prediction of noise attenuation in earplugs has a higher level of uncertainty. However, with accurate age, gender, and ethnicity information, the earplugs can be modelled and designed for each group and the sound attenuation prediction model can be developed to make predictions with higher certainty. Subject Hearing ProtectorsEarplugsHearing ProtectionPredictionGaussian Process Regression To reference this document use: http://resolver.tudelft.nl/uuid:225e16ee-2254-44dd-9047-28314686f0a2 Part of collection Student theses Document type master thesis Rights © 2022 Stephy Annie Curie Rakesh Arya Files PDF Stephy_Thesis_.pdf 22.51 MB Close viewer /islandora/object/uuid:225e16ee-2254-44dd-9047-28314686f0a2/datastream/OBJ/view