Work place: Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342
Research Interests: Image Processing, Image Manipulation, Image Compression, Natural Language Processing, Computational Learning Theory, Graph and Image Processing, Data Structures and Algorithms, Programming Language Theory
Humayra Ferdous was born in Kishoreganj in 1997. She received her B.Sc degree in CSE from Jahangirnagar University in 2019 and completed her M.Sc degree from the same University in 2020. She recently joined as a lecturer at the Department of CSE at Bangladesh University of Business and Technology, Dhaka, Bangladesh. Her research interests cover several aspects of machine learning, deep learning, data science, natural language processing, and image processing.
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.[...] Read more.
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