Active noise cancellation using atrous scaled convolution recurrent neural networks
| dc.contributor.author | Benipal, Sukhpreet | |
| dc.contributor.examiningcommittee | Bassuoni, Mohamed (Civil Engineering) Liang, Xihui (Mechanical Engineering) | en_US |
| dc.contributor.supervisor | Cha, Young-Jin (Civil Engineering) | en_US |
| dc.date.accessioned | 2021-01-12T16:10:09Z | |
| dc.date.available | 2021-01-12T16:10:09Z | |
| dc.date.copyright | 2021-01-11 | |
| dc.date.issued | 2020-12-23 | en_US |
| dc.date.submitted | 2021-01-05T01:42:07Z | en_US |
| dc.date.submitted | 2021-01-12T04:05:16Z | en_US |
| dc.degree.discipline | Civil Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | It 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.description.note | February 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35204 | |
| dc.rights | open access | en_US |
| dc.subject | Active noise cancellation | en_US |
| dc.subject | deep learning | en_US |
| dc.title | Active noise cancellation using atrous scaled convolution recurrent neural networks | en_US |
| dc.type | master thesis | en_US |