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All Journal International Journal of Advances in Applied Sciences Jurnal Ilmu dan Teknologi Kelautan Tropis
Rahman, Md. Saifur
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Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Pitch extraction using discrete cosine transform based power spectrum method in noisy speech Sunzida, Humaira; Parvin, Nargis; Jeba, Jafrin Akhter; Chi, Sulin; Ali, Md. Shiplu; Rahman, Moinur; Rahman, Md. Saifur
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp955-965

Abstract

The pitch period is a key component of many speech analysis research projects. In real-world applications, voice data is frequently gathered in noisy surround- ings, therefore algorithms must be able to manage background noise well in order to estimate pitch accurately. Despite advancements, many state-of–the-art algorithms struggle to deliver adequate results when faced with low signal-to- noise ratios (SNRs) in processing noisy speech signals. This research proposes an effective concept specifically designed for speech processing applications, particularly in noisy conditions. To achieve this goal, we introduce a fundamen- tal frequency extraction algorithm designed to tolerate non-stationary changes in the amplitude and frequency of the input signal. In order to improve the extrac- tion accuracy, we also use a cumulative power spectrum (CPS) based on discrete cosine transform (DCT) rather than conventional power spectrum. We enhance extraction accuracy of our method by utilizing shorter sub-frames of the input signal to mitigate the noise characteristics present in speech signals. According to the experimental results, our proposed technique demonstrates superior per- formance in noisy conditions compared to other existing state-of-the-art meth- ods without utilizing any kind of post-processing techniques.
Long-Term Monitoring of Mangrove Resilience in the Sundarbans after Cyclone Sidr and Aila using Landsat-Derived Vegetation Indices Rahman, Md. Saifur; Rahman, Md. Mostafizur; Rahman, Syed Hafizur
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 17 No. 2 (2025): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v17i2.64452

Abstract

The present work aims at assessing vegetation patterns and of the recovery process over the long term (2006 to 2025) in the Sundarbans mangroves based on the NDVI and SAVI. Landsat 5 TM and Landsat 8 OLI surface reflectance images were processed in Google Earth Engine to derive seasonal composites for the dry season (December–February). A supervised classification method was used to delineate five land-cover classes, namely water bodies, bare soil, sparse, intermediate, and dense vegetation. Accuracy assessment was carried out by visual interpretation of the sample points by using Google Earth Pro where overall accuracy was in the 88–93% over the entire study period. In 2006, dense vegetation was the most dominant (~68%) and sparse and intermediate other categories had low frequency and water bodies covered 21% of plots. For post-Sidr in 2008, nearly all plants showed more severe damage (76-79%). Post-Aila (2010) data suggested continuous intermediate (46%) and sparse (25%) vegetation cover but with negligible closed canopy. During 2015, the dense vegetation recovered to 60%, and dynamic changes among dense, intermediate, and sparse vegetation areas emerged, and the area of dense vegetation was up to 67% in 2025 indicating that the long-term restoration exhibits space heterogeneity. NDVI was effective for monitoring the overall trend of large scale canopy, while SAVI was able to capture very small scale regeneration and understory growth. The findings show the impressive resilience of the Sundarbans and the significance of such key ecological processes as canopy recovery and succession, and the need for more adaptive management to improve mangrove resilience in cyclone-prone coastal areas.
Fundamental frequency extraction by utilizing modified BaNa in noisy speech Saha, Arpita; Parvin, Nargis; Rahman, Md. Saifur; Rahman, Moinur; Chowdhury, Any
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp515-529

Abstract

A sound’s pitch can be largely understood and perceived by using its fundamental frequency. Multiple algorithms have been developed for extracting fundamental frequency, and the choice of which one to employ depends on the noise and features of the signal. Therefore, for an accurate fundamental frequency estimate, the noise resistance of the algorithm becomes even more crucial. Still, many of the most advanced algorithms fail to produce acceptable results when faced with loud speech recordings that have low signal-to-noise ratios (SNRs). In this research paper, we focus on the harmonic selection step in BaNa method, which is one of the vital parts for enhancing the extraction accuracy of fundamental frequency (F0) in noisy situations. BaNa algorithm always emphasizes 5 harmonics on average for both male and female speakers. However, our observation reveals that relying on 5 harmonics is inadequate for male speakers in noisy conditions. Thus, we propose a new idea based on BaNa that separately utilizes the 3 harmonics for male speakers and 5 harmonics for female speakers to achieve accurate pitch extraction within noisy environments. The results demonstrate that our proposed approach attains the lowest rate of gross pitch error (GPE) across various noise types and SNR levels.
Co-Authors Ali, Md. Shiplu Chi, Sulin Chowdhury, Any Jeba, Jafrin Akhter Md. Mostafizur Rahman Parvin, Nargis Rahman, Moinur Rahman, Syed Hafizur Saha, Arpita Sunzida, Humaira