# EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection

arXiv: https://arxiv.org/abs/2509.01153 · HTML: https://mlsanity.com/paper/2509.01153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [A Framework for the Robust Evaluation of Sound Event Detection](https://mlsanity.com/paper/1910.08440/) (2019)
- [Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification](https://mlsanity.com/paper/2305.14032/) (2023)
- [Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning](https://mlsanity.com/paper/2210.16192/) (2022)
- [A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram](https://mlsanity.com/paper/2009.04402/) (2020)
- [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)
- [You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection](https://mlsanity.com/paper/2109.00962/) (2021)
- [Convolutional neural network for breathing phase detection in lung sounds](https://mlsanity.com/paper/1903.10251/) (2019)
- [R-CRNN: Region-based Convolutional Recurrent Neural Network for Audio Event Detection](https://mlsanity.com/paper/1808.06627/) (2018)
- [Improving Sound Event Detection Metrics: Insights from DCASE 2020](https://mlsanity.com/paper/2010.13648/) (2020)
- [Sound Event Detection Utilizing Graph Laplacian Regularization with Event Co-occurrence](https://mlsanity.com/paper/2004.12046/) (2020)
- [A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training](https://mlsanity.com/paper/2107.04229/) (2021)
- [Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification](https://mlsanity.com/paper/2310.03889/) (2023)

## Abstract (arXiv)

Auscultation is a key method for early diagnosis of respiratory and pulmonary diseases, relying on skilled healthcare professionals. However, the process is often subjective, with variability between experts. As a result, numerous deep learning-based automatic classification methods have emerged, most of which focus on respiratory sound classification. In contrast, research on respiratory sound event detection remains limited. Existing sound event detection methods typically rely on frame-level predictions followed by post-processing to generate event-level outputs, making interval boundaries challenging to learn directly. Furthermore, many approaches can only handle fixed-length audio, limiting their applicability to variable-length respiratory sounds. Additionally, the impact of respiratory sound location information on detection performance has not been extensively explored. To address these issues, we propose a graph neural network-based framework with anchor intervals, capable of handling variable-length audio and providing more precise temporal localization for abnormal respiratory sound events. Our method improves both the flexibility and applicability of respiratory sound detection. Experiments on the SPRSound 2024 and HF Lung V1 datasets demonstrate the effectiveness of the proposed approach, and incorporating respiratory position information enhances the discrimination between abnormal sounds. The reference implementation is available at https://github.com/chumingqian/EzhouNet.