mlsanityData to 7 Oct 2026

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

arXiv 2301.09362 ↗First posted 23 Jan 2023cs.SD cs.LG eess.AScited by 7

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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.

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

Builds on

25 of 219 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  6. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  7. 2022A Survey of Machine Unlearningcited by 298
  8. 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
  9. 2021Adaptive Machine Unlearningcited by 164
  10. 2018Deep Neural Decision Treescited by 72
  11. 2021The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classificationcited by 25
  12. 2021Prototype Learning for Interpretable Respiratory Sound Analysiscited by 21
13 more
  1. 2022Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysiscited by 13
  2. 2022Detecting Rumours with Latency Guarantees using Massive Streaming Datacited by 12
  3. 2017Recognizing Abnormal Heart Sounds Using Deep Learningcited by 9
  4. 2018Short-segment heart sound classification using an ensemble of deep convolutional neural networkscited by 9
  5. 2022Model-Agnostic and Diverse Explanations for Streaming Rumour Graphscited by 9
  6. 2020Heart Sound Segmentation using Bidirectional LSTMs with Attentioncited by 6
  7. 2022Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognitioncited by 6
  8. 2018Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detectioncited by 4
  9. 2020Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencodercited by 4
  10. 2021A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separationcited by 4
  11. 2018An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classificationcited by 3
  12. 2018A Markov-Switching Model Approach to Heart Sound Segmentation and Classificationcited by 3
  13. 2021Heart Sound Classification Considering Additive Noise and Convolutional Distortioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Exploring Finetuned Audio-LLM on Heart Murmur Featurescited by 5
  2. 2025Benchmarking machine learning for bowel sound pattern classification from tabular features to pretrained modelscited by 1
  3. 2025Scattering Transformer: A Training-Free Transformer Architecture for Heart Murmur Detectioncited by 1
  4. 2025Model-Free Counterfactual Subset Selection at Scalecited by 0
  5. 2026PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classificationcited by 0
  6. 2026Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordingscited by 0
  7. 2026FINER-SQL: Boosting Small Language Models for Text-to-SQLcited by 0