G
Goutam Saha
Researcher at Indian Institute of Technology Kharagpur
Publications - 96
Citations - 2584
Goutam Saha is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Computer science & Speaker recognition. The author has an hindex of 24, co-authored 73 publications receiving 1996 citations. Previous affiliations of Goutam Saha include Indian Institutes of Technology.
Papers
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Journal ArticleDOI
Evaluation of handcrafted features and learned representations for the classification of arrhythmia and congestive heart failure in ECG
TL;DR: In this paper , the authors employed a two-fold approach to classify arrhythmia, congestive heart failure, and normal sinus rhythm using ECG fragments using a traditional hand-crafted feature based model which involves extraction of a number of linear and non-linear features from the ECG fragment.
Proceedings ArticleDOI
Low-Rank Kernelized Graph-based Clustering using Multiple Views
TL;DR: A novel low-rank multi-view multi-kernel graph-based clustering framework (LRMVMKC) is proposed, where the similarity nature of kernel matrices are exploited by low- rank optimal kernel learning and the clustering performances are boosted by using multiple views that provide different partial information about a given data set.
Journal ArticleDOI
A system for behavior prediction based on neural signals
TL;DR: This paper proposes a pattern recognition system to classify/predict animal behavior based on hippocampal signals that takes into account the practical constraints during acquisition of the neural signal.
Journal Article
Speech Enhancement by Marginal Statistical Characterization in the Log Gabor Wavelet Domain
Suman Senapati,Goutam Saha +1 more
TL;DR: This work presents a fusion of Log Gabor Wavelet and Maximum a Posteriori estimator as a speech enhancement tool for acoustical background noise reduction and shows a higher improvement in Segmental Signal-to-Noise Ratio (S-SNR) and lower Log-Spectral Distortion (LSD) in two different noisy environments compared to other estimators.
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Automatic lung sound cycle extraction from single and multichannel acoustic recordings
TL;DR: A novel signal processing based method is proposed for extraction of LSCs automatically by automated segmentation of LSS without using any additional sensor, and is found to be superior when compared with a recently proposed method.