# Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks

arXiv: https://arxiv.org/abs/1907.05708 · HTML: https://mlsanity.com/paper/1907.05708/

## Key facts

- Cited by 13 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 13 of 13)

- [Lung Sound Classification Using Co-tuning and Stochastic Normalization](https://mlsanity.com/paper/2108.01991/) (2021)
- [Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1](https://mlsanity.com/paper/2102.03049/) (2021)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks](https://mlsanity.com/paper/2104.14921/) (2021)
- [The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence](https://mlsanity.com/paper/2302.00225/) (2023)
- [An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies](https://mlsanity.com/paper/2303.04104/) (2023)
- [Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data](https://mlsanity.com/paper/2402.02183/) (2024)
- [Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection](https://mlsanity.com/paper/2107.04226/) (2021)
- [Variational Autoencoders for Anomaly Detection in Respiratory Sounds](https://mlsanity.com/paper/2208.03326/) (2022)
- [Chronic Obstructive Pulmonary Disease Prediction Using Deep Convolutional Network](https://mlsanity.com/paper/2411.02449/) (2024)
- [A Multi-Stage Hybrid CNN-Transformer Network for Automated Pediatric Lung Sound Classification](https://mlsanity.com/paper/2507.20408/) (2025)
- [Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals](https://mlsanity.com/paper/2512.00563/) (2025)

## Abstract (arXiv)

Respiratory diseases are among the most common causes of severe illness and death worldwide. Prevention and early diagnosis are essential to limit or even reverse the trend that characterizes the diffusion of such diseases. In this regard, the development of advanced computational tools for the analysis of respiratory auscultation sounds can become a game changer for detecting disease-related anomalies, or diseases themselves. In this work, we propose a novel learning framework for respiratory auscultation sound data. Our approach combines state-of-the-art feature extraction techniques and advanced deep-neural-network architectures. Remarkably, to the best of our knowledge, we are the first to model a recurrent-neural-network based learning framework to support the clinician in detecting respiratory diseases, at either level of abnormal sounds or pathology classes. Results obtained on the ICBHI benchmark dataset show that our approach outperforms competing methods on both anomaly-driven and pathology-driven prediction tasks, thus advancing the state-of-the-art in respiratory disease analysis.