Fahima Tabassum

Work place: Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342

E-mail: fahima@juniv.edu

Website: https://orcid.org/0000-0002-0183-3907

Research Interests: Computer systems and computational processes, Computational Science and Engineering


FahimaTabassum has completed her B.Sc. (Hons.) from the department of Computer Science and Engineering, Jahangirnagar University,Savar, Dhaka, Bangladesh in 2003 and M.S from the same department in 2010. She currently is pursuing her Ph.D. degree at the same department. She has a number of publications in different reputed journals. She is also working as a Professor at the Institute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, Bangladesh

Author Articles
The Performance Analysis of Digital Filters and ANN in De-noising of Speech and Biomedical Signal

By Humayra Ferdous Sarwar Jahan Fahima Tabassum Md. Imdadul Islam

DOI: https://doi.org/10.5815/ijigsp.2023.01.06, Pub. Date: 8 Feb. 2023

A huge number of algorithms are found in recent literature to de-noise a signal or enhancement of signal. In this paper we use: static filters, digital adaptive filters, discrete wavelet transform (DWT), backpropagation, Hopfield neural network (NN) and convolutional neural network (CNN) to de-noise both speech and biomedical signals. The relative performance of ten de-noising methods of the paper is measured using signal to noise ratio (SNR) in dB shown in tabular form. The objective of this paper is to select the best algorithm in de-noising of speech and biomedical signals separately. In this paper we experimentally found that, the backpropagation NN is the best for de-noising of biomedical signal and CNN is found as the best for de-noising of speech signal, where the processing time of CNN is found three times higher than that of backpropagation.

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