  In this work, we propose an ensemble of classifiers to distinguish between
various degrees of abnormalities of the heart using Phonocardiogram (PCG)
signals acquired using digital stethoscopes in a clinical setting, for the
INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats
SubChallenge. Our primary classification framework constitutes a convolutional
neural network with 1D-CNN time-convolution (tConv) layers, which uses features
transferred from a model trained on the 2016 Physionet Heart Sound Database. We
also employ a Representation Learning (RL) approach to generate features in an
unsupervised manner using Deep Recurrent Autoencoders and use Support Vector
Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we
utilize an SVM classifier on a high-dimensional segment-level feature extracted
using various functionals on short-term acoustic features, i.e., Low-Level
Descriptors (LLD). An ensemble of the three different approaches provides a
relative improvement of 11.13% compared to our best single sub-system in terms
of the Unweighted Average Recall (UAR) performance metric on the evaluation
dataset.
