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

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