Author
Goutam Saha
Other affiliations: Indian Institutes of Technology
Bio: Goutam Saha is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Speaker recognition & Phonocardiogram. 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 published on a yearly basis
Papers
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28 Mar 2013
TL;DR: In this article, the boundaries of two primary heart sounds, S1 and S2 events are estimated using Hilbert transform method and a statistical approach using an adaptive threshold value which is calculated from the first and second-order moments of the heart sound or Phonocardiogram signal (PCG) envelope.
Abstract: A computerized cardiac disorders classification system needs a proper boundary estimated cardiac cycle of recorded heart sound signals. In the proposed algorithm the boundaries of two primary heart sounds, S1 and S2 events are estimated using Hilbert transform method and a statistical approach. This method uses an adaptive threshold value which is calculated from the first- and second-order moments of the heart sound or Phonocardiogram signal (PCG) envelope. Here, Hilbert transform is used for extracting the envelope of the PCG signal and a threshold is applied to detect the boundary regions of the primary events, S1 and S2, of the same signal. The performance of the algorithm is evaluated for normal and five commonly occurring pathological cases. The proposed method is computationally fast and obtained an accuracy of 97.24 percent.
16 citations
TL;DR: This work proposes a novel heuristic FS approach, Conditional Priority Coverage Maximization (CPCM), which seeks to leverage the local information provided by the small set of instances to avoid the selection of redundant features, while selecting relevant ones.
15 citations
23 Sep 2007
TL;DR: Experimental results show that the proposed estimator yields a higher improvement in identification accuracy compared to other estimators on popular Gaussian Mixture Model (GMM) based speaker model and Mel-Frequency Cepstral Coefficient (MFCC) features.
Abstract: Real world Speaker Identification (SI) application differs from ideal or laboratory conditions causing perturbations that leads to a mismatch between the training and testing environment and degrade the performance drastically. Many strategies have been adopted to cope with acoustical degradation; wavelet based Bayesian marginal model is one of them. But Bayesian marginal models cannot model the inter-scale statistical dependencies of different wavelet scales. Simple nonlinear estimators for wavelet based denoising assume that the wavelet coefficients in different scales are independent in nature. However wavelet coefficients have significant inter-scale dependency. This paper enhances this inter-scale dependency property by a Circularly Symmetric Probability Density Function (CS-PDF) related to the family of Spherically Invariant Random Processes (SIRPs) in Log Gabor Wavelet (LGW) domain and corresponding joint shrinkage estimator is derived by Maximum a Posteriori (MAP) estimator. A framework is proposed based on these to denoise speech signal for automatic speaker identification problems. The robustness of the proposed framework is tested for Text Independent Speaker Identification application on 100 speakers of POLYCOST and 100 speakers of YOHO speech database in three different noise environments. Experimental results show that the proposed estimator yields a higher improvement in identification accuracy compared to other estimators on popular Gaussian Mixture Model (GMM) based speaker model and Mel-Frequency Cepstral Coefficient (MFCC) features. Keywords—Speaker Identification, Log Gabor Wavelet, Bayesian Bivariate Estimator, Circularly Symmetric Probability Density Function, SIRP.
14 citations
TL;DR: SST can capture useful time-frequency information from PCG to facilitate CAD detection and the proposed fusion framework using SST and spectral features in a multichannel PCG acquisition platform performs better than other PCG based approaches.
14 citations
TL;DR: In this article, a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process were introduced for combining multiple systems based on different features and classifiers.
13 citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
4,948 citations
TL;DR: In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter based denoising methods are compared based on signals from mechanical defects, and the comparison result reveals that wavelet filters are more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet transform has a better performance on smooth signal detection.
1,104 citations
1,008 citations
TL;DR: Various procedures used in the analysis of circadian rhythms at the populational, organismal, cellular and molecular levels are reviewed.
Abstract: This article reviews various procedures used in the analysis of circadian rhythms at the populational, organismal, cellular and molecular levels. The procedures range from visual inspection of time plots and actograms to several mathematical methods of time series analysis. Computational steps are described in some detail, and additional bibliographic resources and computer programs are listed.
583 citations