S
Samarendra Dandapat
Researcher at Indian Institute of Technology Guwahati
Publications - 200
Citations - 2877
Samarendra Dandapat is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 21, co-authored 179 publications receiving 2205 citations. Previous affiliations of Samarendra Dandapat include Indian Institute of Technology Kanpur & Nanyang Technological University.
Papers
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Multiscale Entropy-Based Weighted Distortion Measure for ECG Coding
TL;DR: A novel objective distortion measure is proposed for compressed electrocardiogram (ECG) signals that works better than the other existing measures and correlates well with the subjective assessments.
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Analysis and Classification of Cold Speech Using Variational Mode Decomposition
TL;DR: The proposed feature outperforms the linear prediction coefficients (LPC), mel frequency cepstral coefficients (MFCC), Teager energy operator (TEO) based feature and ComParE feature sets (IS09-emotion and IS13-ComParE) and shows an average recognition rate of 90.02 percent for IITG cold speech database and 66.84 percent for URTIC database.
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Myocardial Infarction Severity Stages Classification From ECG Signals Using Attentional Recurrent Neural Network
TL;DR: This paper proposes a novel multi-lead diagnostic attention-based recurrent neural network (MLDA-RNN) for automated diagnosis of the three MI severity stages from HC subjects and achieves an overall accuracy of 97.79% without compromising on the class-wise detection rates.
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Speaker recognition under stressed condition
TL;DR: This paper presents the feature analysis and design of compensators for speaker recognition under stressed speech conditions, and four VQ based novel compensation techniques are proposed and evaluated for improvement of speaker Recognition under stressed condition.
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Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features
TL;DR: The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes.