Print Email Facebook Twitter Improving accuracy of sound reflection estimation using neural networks Title Improving accuracy of sound reflection estimation using neural networks Author Scholtens, Ekko (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Eisemann, E. (mentor) Martinez, Jorge (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-02-03 Abstract In this paper, we present a study to improve using neural networks for acoustic reflection localization. Our study focuses on the reimplementation of the proposed neural network model by Bologni et al. and investigates the effect of adding a third microphone to the microphone array. We reimplemented and trained the neural network using the same framework and hyperparameters as the original model, and then evaluated it using the same metrics. Our results show that the addition of a third microphone improves the amount of detected sources from 43% to 58%, it also improved the front-back ambiguity from 25% to 18%. Conclusively, have demonstrated the potential benefits of adding a third microphone to the neural network approach for acoustic reflection localization. Subject Neural Networksound reflectionAcoustics To reference this document use: http://resolver.tudelft.nl/uuid:aa7c5390-d96f-4fa0-9793-01ead0118f38 Part of collection Student theses Document type bachelor thesis Rights © 2023 Ekko Scholtens Files PDF CSE3000_Final_Paper_Ekko_ ... oltens.pdf 615.08 KB Close viewer /islandora/object/uuid:aa7c5390-d96f-4fa0-9793-01ead0118f38/datastream/OBJ/view