# Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds

arXiv: https://arxiv.org/abs/2012.13668 · HTML: https://mlsanity.com/paper/2012.13668/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies](https://mlsanity.com/paper/2303.04104/) (2023)
- [AI-enabled Sound Pattern Recognition on Asthma Medication Adherence: Evaluation with the RDA Benchmark Suite](https://mlsanity.com/paper/2205.15360/) (2022)
- [Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection](https://mlsanity.com/paper/2107.04226/) (2021)
- [A Multi-Stage Hybrid CNN-Transformer Network for Automated Pediatric Lung Sound Classification](https://mlsanity.com/paper/2507.20408/) (2025)

## Abstract (arXiv)

This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C- DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.