# Variational Autoencoders for Anomaly Detection in Respiratory Sounds

arXiv: https://arxiv.org/abs/2208.03326 · HTML: https://mlsanity.com/paper/2208.03326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 5 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks](https://mlsanity.com/paper/1907.05708/) (2019)
- [Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases](https://mlsanity.com/paper/2002.03894/) (2020)
- [Music generation with variational recurrent autoencoder supported by history](https://mlsanity.com/paper/1705.05458/) (2017)
- [An Ensemble of Deep Learning Frameworks Applied For Predicting Respiratory Anomalies](https://mlsanity.com/paper/2201.03054/) (2022)
- [Influence of Event Duration on Automatic Wheeze Classification](https://mlsanity.com/paper/2011.02874/) (2020)
- [Acoustics-specific Piano Velocity Estimation](https://mlsanity.com/paper/2203.16294/) (2022)

## Abstract (arXiv)

This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases. Various types of pathologies may affect the respiratory system, potentially leading to severe diseases and, in certain cases, death. In general, effective prevention practices are considered as major actors towards the improvement of the patient's health condition. The proposed method strives to realize an easily accessible tool for the automatic diagnosis of respiratory diseases. Specifically, the method leverages Variational Autoencoder architectures permitting the usage of training pipelines of limited complexity and relatively small-sized datasets. Importantly, it offers an accuracy of 57 %, which is in line with the existing strongly-supervised approaches.