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Comparison of Backpropagation and Hopfield Model in De-noising of Speech Signal

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dc.contributor.author Ferdous, Md. Robayet
dc.contributor.author Akter, Mokarromah
dc.contributor.author Islam, Nowsadul
dc.date.accessioned 2021-12-23T04:18:58Z
dc.date.available 2021-12-23T04:18:58Z
dc.date.issued 2019-09-08
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3352
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electronics and Telecommunication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract In this project work, we have used two algorithms of Neural Network (NN): Backpropagation and Hopfield NN to de-noise speech signal. The backpropagation algorithm is found suitable to remove random noise but very poor in the removal of awgn (Additive White Gaussian Noise). The Hopfield NN shows completely reverse performance i.e. suitable for awgn but very poor for random noise. The performance of both algorithms is measured graphically with an original recovered signal, MSE, the convergence of regression and error histogram. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00220
dc.subject Hopfield Model in De-noising of Speech Signal en_US
dc.title Comparison of Backpropagation and Hopfield Model in De-noising of Speech Signal en_US
dc.type Thesis en_US


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