Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification (INTERSPEECH 2023)

Implementation of IEEE Access paper - Lung Sound Recognition Algorithm Based on VGGish-BiGRU

[ICASSP 2024] Multi-View Spectrogram Transformer for Respiratory Sound Classification

RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds (IEEE TIM-2024)

RespireNet is an innovative web-based application that harnesses the capabilities of deep learning and Mel-frequency cepstral coefficients (MFCC) as a feature extraction technique for accurate respiratory disease prediction. The primary objective of this user-friendly web application is to facilitate early detection.

VGAResNet: A Unified Visibility Graph Adjacency Matrix-Based Residual Network for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds

AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds (INTERSPEECH 2024)

AsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound Classification (IEEE MeMeA-2024)

ILDNet: A Novel Deep Learning Framework for Interstitial Lung Disease Identification Using Respiratory Sounds (IEEE SPCOM-2024)

A Python-based dataset of high-quality respiratory sound recordings, annotated for machine learning tasks focused on detecting lung conditions like wheezes and crackles. It includes preprocessed audio, annotations, and subject metadata for research in respiratory health.

COPD severity grading using lung sounds and machine learning

R2Rest: A Novel Deep Learning Framework for estimating respiration rate from Respiratory Sounds (IEEE SPL-2025)

Signal processing project repo

Code accompanying ESANN 2025 submission "Exploring Model Architectures for Real-Time Lung Sound Event Detection". Dataset used was ICBHI 2017.

RECONOCIMIENTO Y CLASIFICACIÓN DE SONIDOS RESPIRATORIOS EN TIEMPO REAL: Utiliza deep learning y procesamiento de señales con el dataset KAGGLE para identificar patrones respiratorios clínicamente relevantes.

Pulmo-TS2ONN: A Novel Triple Scale Self Operational Neural Network for Pulmonary Disorder Detection Using Respiratory Sounds (IEEE TIM-2024)

A Novel Multi-Head Self-Organized Operational Neural Network Architecture for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds (IEEE TASLP-2024)