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

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Humayra Ferdous 1 Sarwar Jahan 2 Fahima Tabassum 3,* Md. Imdadul Islam 1

1. Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342

2. Department of Electronics and Communications Engineering, East West University, Aftabnagar, Dhaka-1212

3. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342

* Corresponding author.


Received: 9 Apr. 2022 / Revised: 13 Jun. 2022 / Accepted: 15 Aug. 2022 / Published: 8 Feb. 2023

Index Terms

LMS, process time, error histogram, DWT and SNR.


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.

Cite This Paper

Humayra Ferdous, Sarwar Jahan, Fahima Tabassum, Md. Imdadul Islam, "The Performance Analysis of Digital Filters and ANN in De-noising of Speech and Biomedical Signal", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.1, pp. 63-78, 2023. DOI:10.5815/ijigsp.2023.01.06


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