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Your search keyword '"Chakraverty, Snehashish"' showing total 475 results
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475 results on '"Chakraverty, Snehashish"'

5. Fractional Legendre Kernel Functions: Theory and Application

Author

Azmoon, Amirreza, Chakraverty, Snehashish, Kumar, Sunil, Gowda, G. D. Veerappa, Editor-in-Chief, Kesavan, S., Editor-in-Chief, Nekka, Fahima, Editor-in-Chief, Khan, Akhtar A., Editorial Board Member, Rangarajan, Govindan, Editorial Board Member, Balachandran, K., Editorial Board Member, Sreenivasan, K. R., Editorial Board Member, Brokate, Martin, Editorial Board Member, Nashed, M. Zuhair, Editorial Board Member, Gupta, N. K., Editorial Board Member, Zahra, Noore, Editorial Board Member, Manchanda, Pammy, Editorial Board Member, Lozi, René Pierre, Editorial Board Member, Aslan, Zafer, Editorial Board Member, Rad, Jamal Amani, editor, Parand, Kourosh, editor, and Chakraverty, Snehashish, editor

Published
2023
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11. Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach

Author

Roy, Tanmoy, Marwala, Tshilidzi, and Chakraverty, Snehashish

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Precise detection of speech endpoints is an important factor which affects the performance of the systems where speech utterances need to be extracted from the speech signal such as Automatic Speech Recognition (ASR) system. Existing endpoint detection (EPD) methods mostly uses Short-Term Energy (STE), Zero-Crossing Rate (ZCR) based approaches and their variants. But STE and ZCR based EPD algorithms often fail in the presence of Non-speech Sound Artifacts (NSAs) produced by the speakers. Algorithms based on pattern recognition and classification techniques are also proposed but require labeled data for training. A new algorithm termed as Wavelet Convolution based Speech Endpoint Detection (WCSEPD) is proposed in this article to extract speech endpoints. WCSEPD decomposes the speech signal into high-frequency and low-frequency components using wavelet convolution and computes entropy based thresholds for the two frequency components. The low-frequency thresholds are used to extract voiced speech segments, whereas the high-frequency thresholds are used to extract the unvoiced speech segments by filtering out the NSAs. WCSEPD does not require any labeled data for training and can automatically extract speech segments. Experiment results show that the proposed algorithm precisely extracts speech endpoints in the presence of NSAs., Comment: 25 Pages

Published
2018
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22. Preliminaries

Author

Chakraverty, Snehashish, Rout, Saudamini, Chakraverty, Snehashish, and Rout, Saudamini

Published
2020
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27. Introduction

Author

Chakraverty, Snehashish, Rout, Saudamini, Chakraverty, Snehashish, and Rout, Saudamini

Published
2020
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32. Parametric Optimization for Fully Fuzzy Linear Programming Problems with Triangular Fuzzy Numbers.

Author

Bhowmick, Aliviya, Chakraverty, Snehashish, and Chatterjee, Subhashish

Subjects
*MATHEMATICAL programming, *LINEAR programming, *FUZZY integrals, *FUZZY numbers, *FUZZY arithmetic
Abstract

This paper presents a new approach for solving FFLP problems using a double parametric form (DPF), which is critical in decision-making scenarios characterized by uncertainty and imprecision. Traditional linear programming methods often fall short in handling the inherent vagueness in real-world problems. To address this gap, an innovative method has been proposed which incorporates fuzzy logic to model the uncertain parameters as TFNs, allowing for a more realistic and flexible representation of the problem space. The proposed method stands out due to its integration of fuzzy arithmetic into the optimization process, enabling the handling of fuzzy constraints and objectives directly. Unlike conventional techniques that rely on crisp approximations or the defuzzification process, the proposed approach maintains the fuzziness throughout the computation, ensuring that the solutions retain their fuzzy characteristics and better reflect the uncertainties present in the input data. In summary, the proposed method has the ability to directly incorporate fuzzy parameters into the optimization framework, providing a more comprehensive solution to FFLP problems. The main findings of this study underscore the method's effectiveness and its potential for broader application in various fields where decision-making under uncertainty is crucial. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Hebbian Learning Rule

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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40. Perceptron Learning Rule

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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41. Defuzzification

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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42. Fuzzy Functions

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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43. Fuzzy Relations

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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44. Fuzzy Sets

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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45. Fuzzy Numbers

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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46. Intervals

Author

Chakraverty, Snehashish, Sahoo, Deepti Moyi, Mahato, Nisha Rani, Chakraverty, Snehashish, Sahoo, Deepti Moyi, and Mahato, Nisha Rani

Published
2019
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48. Advancements and Role of Emotion Recognition in the 4th Industrial Revolution

Author

Roy, Tanmoy, Marwala, Tshilidzi, Chakraverty, Snehashish, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Doorsamy, Wesley, editor, Paul, Babu Sena, editor, and Marwala, Tshilidzi, editor

Published
2020
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