# A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

arXiv: https://arxiv.org/abs/2301.09362 · HTML: https://mlsanity.com/paper/2301.09362/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 219 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Interpretable Counterfactual Explanations Guided by Prototypes](https://mlsanity.com/paper/1907.02584/) (2019)
- [Adaptive Machine Unlearning](https://mlsanity.com/paper/2106.04378/) (2021)
- [Deep Neural Decision Trees](https://mlsanity.com/paper/1806.06988/) (2018)
- [The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification](https://mlsanity.com/paper/2108.00813/) (2021)
- [Prototype Learning for Interpretable Respiratory Sound Analysis](https://mlsanity.com/paper/2110.03536/) (2021)
- [Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis](https://mlsanity.com/paper/2203.16141/) (2022)
- [Detecting Rumours with Latency Guarantees using Massive Streaming Data](https://mlsanity.com/paper/2205.06580/) (2022)
- [Recognizing Abnormal Heart Sounds Using Deep Learning](https://mlsanity.com/paper/1707.04642/) (2017)
- [Short-segment heart sound classification using an ensemble of deep convolutional neural networks](https://mlsanity.com/paper/1810.11573/) (2018)
- [Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs](https://mlsanity.com/paper/2207.08098/) (2022)
- [Heart Sound Segmentation using Bidirectional LSTMs with Attention](https://mlsanity.com/paper/2004.03712/) (2020)
- [Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition](https://mlsanity.com/paper/2203.04696/) (2022)
- [Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection](https://mlsanity.com/paper/1806.05892/) (2018)
- [Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder](https://mlsanity.com/paper/2012.06275/) (2020)
- [A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation](https://mlsanity.com/paper/2109.03275/) (2021)
- [An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification](https://mlsanity.com/paper/1806.06506/) (2018)
- [A Markov-Switching Model Approach to Heart Sound Segmentation and Classification](https://mlsanity.com/paper/1809.03395/) (2018)
- [Heart Sound Classification Considering Additive Noise and Convolutional Distortion](https://mlsanity.com/paper/2106.01865/) (2021)

## Built on by (most cited first, 7 of 7)

- [Exploring Finetuned Audio-LLM on Heart Murmur Features](https://mlsanity.com/paper/2501.13884/) (2025)
- [Benchmarking machine learning for bowel sound pattern classification from tabular features to pretrained models](https://mlsanity.com/paper/2502.15607/) (2025)
- [Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detection](https://mlsanity.com/paper/2509.18424/) (2025)
- [Model-Free Counterfactual Subset Selection at Scale](https://mlsanity.com/paper/2502.08326/) (2025)
- [PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classification](https://mlsanity.com/paper/2601.17080/) (2026)
- [Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings](https://mlsanity.com/paper/2603.13362/) (2026)
- [FINER-SQL: Boosting Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2605.03465/) (2026)

## Abstract (arXiv)

Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning's performance improvement in the era of big data. Deep learning has outperformed classic machine learning in many research fields, as it employs more complex model architectures with a stronger capability of extracting effective representations. Moreover, it has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were carried out before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning published in 2017--2022. This work introduces both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis. Our repository is publicly available at urlhttps://github.com/zhaoren91/awesome-heart-sound-analysis.