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

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dc.contributor.authorFerdous, Md. Robayet
dc.contributor.authorAkter, Mokarromah
dc.contributor.authorIslam, Nowsadul
dc.date.accessioned2021-12-23T04:18:58Z
dc.date.available2021-12-23T04:18:58Z
dc.date.issued2019-09-08
dc.descriptionThis 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
dc.description.abstractIn 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.
dc.identifier.otherhttp://dspace.ewubd.edu:8080/handle/123456789/3352
dc.identifier.urihttp://dspace.ewubd.edu:8080/handle/123456789/3352
dc.language.isoen_US
dc.publisherEast West University
dc.sourceEast West University Institutional Repository
dc.subjectHopfield Model in De-noising of Speech Signal
dc.titleComparison of Backpropagation and Hopfield Model in De-noising of Speech Signal
dc.typeThesis

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