# Heart Sound Classification Considering Additive Noise and Convolutional Distortion

arXiv: https://arxiv.org/abs/2106.01865 · HTML: https://mlsanity.com/paper/2106.01865/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [A Bayesian Data Augmentation Approach for Learning Deep Models](https://mlsanity.com/paper/1710.10564/) (2017)
- [Short-segment heart sound classification using an ensemble of deep convolutional neural networks](https://mlsanity.com/paper/1810.11573/) (2018)
- [Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection](https://mlsanity.com/paper/1806.05892/) (2018)
- [An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification](https://mlsanity.com/paper/1806.06506/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals](https://mlsanity.com/paper/2509.11606/) (2025)

## Abstract (arXiv)

Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both types of distortions are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive and convolutional noise on short-term filterbank-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed on an open-access heart sound abnormality detection dataset involving noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc (mean of sensitivity and specificity) of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The proposed method paves the way towards developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.