Goutam Saha

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.

Interpretation of Heart Sound Signal through Automated Artifact-Free Segmentation

Abstract: Purpose: Digital recording of heart sounds commonly known as Phonocardiogram (PCG) signal, is a convenient primary diagnostic tool for analyzing condition of heart. Phono- cardiogram aids physicians to visualize the acoustic energies that results from mechanical aspect of cardiac activity. PCG signal cycle segmentation is an essential processing step to- wards heart sound signal analysis. Sound artifacts due to inappropriate placement of stetho- scope, body movement, cough etc. makes segmentation difficult. Artifact-free segmented heart sound cycles are convenient for physicians to interpret and it is also useful for computerized automated classification of abnormality. Methods: We have developed a framework which selects good quality heart sound subse- quences which are artifact-free and reused the features involved in this processing in segmenta- tion. In this work, we have used information contained in frequency subbands by decomposing the signal using Discrete Wavelet Packet Transform (DWPT). The algorithm identifies the parts of the signal where artifacts are prominent and it also detects major events in heart sound cycles. Results: The algorithm shows good results when tested on normal and five commonly occur - ring pathological heart sound signals. An average accuracy of 93.71% is registered for artifact- free subsequence selection process. The cycle segmentation algorithm gives an accuracy of 98.36%, 98.18% and 93.97% respectively for three databases used in the experiment. Conclusions: The work provides a solution for artifact-free segmentation of heart sound cy- cles to assist interpretation of heart sound by physicians in objective analysis through record- ing in a computer. It is also useful for development of an automated decision support system on heart sound abnormality.

An Adaptive i-Vector Extraction for Speaker Verification with Short Utterance

Abstract: A prime challenge in automatic speaker verification (ASV) is to improve performance with short speech segments. The variability and uncertainty of intermediate model parameters associated with state-of-the-art i-vector based ASV system, extensively increases in short duration. To compensate increased variability, we propose an adaptive approach for estimation of model parameters. The pre-estimated universal background model (UBM) parameters are used for adaptation. The speaker models i.e., i-vectors are generated with the proposed adapted parameters. The ASV performance with the proposed approach considerably outperformed conventional i-vector based system on publicly available speech corpora, NIST SRE 2010, especially in short duration, as required in real-world applications.

Ensembled Transfer Learning and Multiple Kernel Learning for Phonocardiogram Based Atherosclerotic Coronary Artery Disease Detection

Abstract: Conventional machine learning has paved the way for a simple, affordable, non-invasive approach for Coronary artery disease (CAD) detection using phonocardiogram (PCG). It leaves a scope to explore improvement of performance metrics by fusion of learned representations from deep learning. In this study, we propose a novel, multiple kernel learning (MKL) for their fusion using deep embeddings transferred from pre-trained convolutional neural network (CNN). The proposed MKL, finds optimal kernel combination by maximizing the similarity with ideal kernel and minimizing the redundancy with other basis kernels. Experiments are performed on 960 PCG epochs collected from 40 CAD and 40 normal subjects. The transferred embeddings attain maximum subject-level accuracy of 89.25% with kappa of 0.7850. Later, their fusion with handcrafted features using the proposed MKL gives an accuracy of 91.19% and kappa 0.8238. The study shows the potential of development of high accuracy CAD detection system by using easy to acquire, non-invasive PCG signal.

Pattern Recognition and Machine Learning

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.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

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.

Procedures for numerical analysis of circadian rhythms.

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.