# Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method

arXiv: https://arxiv.org/abs/2407.10828 · HTML: https://mlsanity.com/paper/2407.10828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 15 Jul 2024.

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

- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation](https://mlsanity.com/paper/2102.01243/) (2021)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://mlsanity.com/paper/2108.02456/) (2021)
- [RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting](https://mlsanity.com/paper/2011.00196/) (2020)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [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)
- [FastAudio: A Learnable Audio Front-End for Spoof Speech Detection](https://mlsanity.com/paper/2109.02774/) (2021)
- [EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use](https://mlsanity.com/paper/2207.05508/) (2022)
- [A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism](https://mlsanity.com/paper/2006.09815/) (2020)

## Abstract (arXiv)

This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal breath sound classification through an innovative multi-label learning approach and multi-head attention mechanism. Addressing the issue of class imbalance and lack of diversity in existing respiratory sound datasets, our study employs a lightweight and highly accurate model, using a two-dimensional label set to represent multiple respiratory sound characteristics. Our method achieved a 59.2% ICBHI score in the four-category task on the ICBHI2017 dataset, demonstrating its advantages in terms of lightweight and high accuracy. This study not only improves the accuracy of automatic diagnosis of lung respiratory sound abnormalities but also opens new possibilities for clinical applications.