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dc.contributor.supervisorCha, Young-Jin (Civil Engineering)en_US
dc.contributor.authorBenipal, Sukhpreet
dc.date.accessioned2021-01-12T16:10:09Z
dc.date.available2021-01-12T16:10:09Z
dc.date.copyright2021-01-11
dc.date.issued2020-12-23en_US
dc.date.submitted2021-01-05T01:42:07Zen_US
dc.date.submitted2021-01-12T04:05:16Zen_US
dc.identifier.urihttp://hdl.handle.net/1993/35204
dc.description.abstractIt has been proven that the external environmental noises affect the human mental health and performances of works. To avoid these detrimental effects, different passive and active noise control system had been developed. Moreover, the focus on active noise control has been increased because of availability of efficient circuits and computational power. However, most of the active noise cancellation systems are based on traditional modelling with limited efficiency. However, in this study, I propose a deep learning based active noise cancellation system which can perform well under different environmental noises. It has been shown in this study that the performance of the proposed methodology is superior to traditional and machine learning based models.en_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectActive noise cancellationen_US
dc.subjectdeep learningen_US
dc.titleActive noise cancellation using atrous scaled convolution recurrent neural networksen_US
dc.typemaster thesisen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.degree.disciplineCivil Engineeringen_US
dc.contributor.examiningcommitteeBassuoni, Mohamed (Civil Engineering) Liang, Xihui (Mechanical Engineering)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteFebruary 2021en_US


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