Moinur Rahman

Work place: Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh



Research Interests:


Moinur Rahman received his B.Sc. (Engineering) and M.Sc. (Engineering) degrees in Information and Communication Technology from Comilla University, Cumilla, Bangladesh, in 2018 and 2019, respectively.
In 2022, he joined as a Lecturer in the Department of Computer Science and Engineering, The People's University of Bangladesh, 3/2 Asad Avenue, Dhaka, Bangladesh. Now he is currently serving as a lecturer in the Department of Information Technology, University of Information Technology and Sciences, Baridhara, Dhaka, Bangladesh from March 2023. His current research interests include speech analysis and digital signal processing. He can be contacted at email:

Author Articles
Fundamental Frequency Extraction by Utilizing Accumulated Power Spectrum based Weighted Autocorrelation Function in Noisy Speech

By Nargis Parvin Moinur Rahman Irana Tabassum Ananna Md. Saifur Rahman

DOI:, Pub. Date: 8 Jun. 2024

This research suggests an efficient idea that is better suited for speech processing applications for retrieving the accurate pitch from speech signal in noisy conditions. For this objective, we present a fundamental frequency extraction algorithm and that is tolerant to the non-stationary changes of the amplitude and frequency of the input signal. Moreover, we use an accumulated power spectrum instead of power spectrum, which uses the shorter sub-frames of the input signal to reduce the noise characteristics of the speech signals. To increase the accuracy of the fundamental frequency extraction we have concentrated on maintaining the speech harmonics in their original state and suppressing the noise elements involved in the noisy speech signal. The two stages that make up the suggested fundamental frequency extraction approach are producing the accumulated power spectrum of the speech signal and weighting it with the average magnitude difference function. As per the experiment results, the proposed technique appears to be better in noisy situations than other existing state-of-the-art methods such as Weighted Autocorrelation Function (WAF), PEFAC, and BaNa.

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