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All Journal Journal of Applied Data Sciences International Journal Research on Metaverse
Selvaraj, Poovarasan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Speech Enhancement using Sliding Window Empirical Mode Decomposition with Median Filtering Technique Selvaraj, Poovarasan; Maidin, Siti Sarah; Yang, Qingxue
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.470

Abstract

The Empirical Mode Decomposition is raising significant interest since its first introduction among the nineties. The attention in varied fields such as medical engineering, space analysis, hydrology, synthetic aperture measuring, speech enhancement, watermarking and etc. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by subsequently the least degraded IMF. Hereafter, in this article, SWEMD method is enhanced by using Sliding Window (SW) procedure. This research work has come SDG goals for health and well-being and also this research work concentrated on hearing aid application using noise level adjustment. In this SWEMDH method, the calculation of EMD is performed based on the small and sliding window along with the time axis. For each component, the total of sifting iterations is unwavering by decomposition of many signal windows by standard algorithm and calculating the average amount of sifting steps for each component. The median filter used for removed nonlinear components of this work. SWEMDH technique removed for low frequency Noisy Components. The speech quality was evaluation by the performance matrices of Mean Square Error, Perceptual evaluation of speech quality, signal to noise ratio, peak signal to noise ratio. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
Temporal Analysis of Viewer Engagement in One Piece: Trends in IMDb Ratings Across Arcs and Time Selvaraj, Poovarasan; Yang, Qingxue
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.34

Abstract

This study examines the long-term viewer reception of One Piece, a globally influential anime series, by analyzing 1,122 episodes released between 1999 and 2023 using IMDb data. The research aims to understand how audience engagement and critical reception evolve, and how content structure, specifically narrative arcs and episode types, affects these dynamics. Quantitative analysis was conducted on episode ratings, vote counts, narrative arc segmentation, and episode classification (Canon, Filler, Semi-Filler). The findings reveal a clear upward trend in average episode ratings, increasing from 7.9 in 1999 to over 8.4 in recent years. Viewer participation also grew significantly, with total annual votes rising from 63,939 in 1999 to over 300,000 votes per year after 2020. Canon episodes achieved the highest average rating (8.22) across 996 episodes and garnered a total of 2,630,987 votes. In contrast, Filler episodes averaged 6.62 (90 episodes), while Semi-Filler episodes scored 7.14 (36 episodes). The highest-rated narrative arcs, each comprising at least five episodes, consistently achieved an average rating of 8.7 or higher and demonstrated alignment with major plot developments and emotional climaxes. These results highlight the importance of narrative relevance and continuity in maintaining audience satisfaction and fostering long-term engagement. The increasing vote volume reflects the expansion of One Piece’s global audience and the role of digital platforms in amplifying participatory behavior. This study highlights how serialized media can maintain cultural impact through strategic narrative design and evolving viewer engagement.
Co-Authors Maidin, Siti Sarah Yang, Qingxue