  Automatic heart sound abnormality detection can play a vital role in the
early diagnosis of heart diseases, particularly in low-resource settings. The
state-of-the-art algorithms for this task utilize a set of Finite Impulse
Response (FIR) band-pass filters as a front-end followed by a Convolutional
Neural Network (CNN) model. In this work, we propound a novel CNN architecture
that integrates the front-end bandpass filters within the network using
time-convolution (tConv) layers, which enables the FIR filter-bank parameters
to become learnable. Different initialization strategies for the learnable
filters, including random parameters and a set of predefined FIR filter-bank
coefficients, are examined. Using the proposed tConv layers, we add constraints
to the learnable FIR filters to ensure linear and zero phase responses.
Experimental evaluations are performed on a balanced 4-fold cross-validation
task prepared using the PhysioNet/CinC 2016 dataset. Results demonstrate that
the proposed models yield superior performance compared to the state-of-the-art
system, while the linear phase FIR filterbank method provides an absolute
improvement of 9.54% over the baseline in terms of an overall accuracy metric.
