Development of a Respiratory Sound Labeling Software for Training a Deep Learning-Based Respiratory Sound Analysis Model
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University（国立台湾大学生物医学电子与生物信息学研究所）；Heroic Faith Medical Science Corporation Limited（恒信医学科技股份有限公司）；National Taiwan University-Stanford Joint Program Office of AI in Biotechnology, Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare（国立台湾大学-斯坦福大学生物技术人工智能联合项目办公室 科技部人工智能技术联合研究中心）；Department of Life Science, College of Life Science, National Taiwan University（国立台湾大学生命科学学院生命科学系）；Institute of Biomedical Sciences, Academia Sinica（中央研究院生物医学科学研究所）；Department of Critical Care, Far Eastern Memorial Hospital（远东纪念医院重症监护部）
Respiratory auscultation can help healthcare professionals detect abnormal respiratory conditions if adventitious lung sounds are heard. The state-of-the-art artificial intelligence technologies based on deep learning show great potential in the development of automated respiratory sound analysis. To train a deep learning-based model, a huge number of accurate labels of normal breath sounds and adventitious sounds are needed. In this paper, we demonstrate the work of developing a respiratory sound labeling software to help annotators identify and label the inhalation, exhalation, and adventitious respiratory sound more accurately and quickly. Our labeling software integrates six features from MATLAB Audio Labeler, and one commercial audio editor, RX7. As of October, 2019, we have labeled 9,765 15-second-long audio files of breathing lung sounds, and accrued 34,095 inhalation labels,18,349 exhalation labels, 13,883 continuous adventitious sounds (CASs) labels and 15,606 discontinuous adventitious sounds (DASs) labels, which are significantly larger than previously published studies. The trained convolutional recurrent neural networks based on these labels showed good performance with F1-scores of 86.0% on inhalation event detection, 51.6% on CASs event detection and 71.4% on DASs event detection. In conclusion, our results show that our proposed respiratory sound labeling software could easily pre-define a label, perform one-click labeling, and overall facilitate the process of accurately labeling. This software helps develop deep learning-based models that require a huge amount of labeled acoustic data.
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
School of BioMedical Engineering, Hainan University（海南大学生物医学工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Information and Communication, Hainan University（海南大学信息与通信学院）
Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification
RSC LAB, MODULABS（RSC实验室，MODULABS）；Department of Electronic Engineering, Wonkwang University（圆光大学电子工程系）；AI Convergence Research Institute, Wonkwang University（圆光大学人工智能融合研究所）
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
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（远东纪念医院）；Heroic Faith Medical Science Co. Ltd.（浩信医疗科技股份有限公司）；Avalanche Computing Inc.（雪崩计算公司）；Academia Sinica（中央研究院）；HCC Healthcare Group（HCC医疗集团）
Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses
AI Center, FPT Software Company Limited（FPT软件公司人工智能中心）；Reliable Machine Learning Group（可靠机器学习小组）；School of Computer Science and Information Technology, University College Cork, Ireland（University College Cork信息科学与技术学院）
Multi-View Spectrogram Transformer for Respiratory Sound Classification
School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机科学学院）；Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China（宁波诺丁汉大学诺丁汉卓越研究与创新研究院）；School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；RSC LAB（RSC实验室）；Wonkwang University（Wonkwang大学）
Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice
Pediatric Asthma Detection with Googles HeAR Model: An AI-Driven Respiratory Sound Classifier
Kent State University（肯特州立大学）；Northeastern University（东北大学）；Department of Computer Science, Cleveland State University（计算机科学系，克利夫兰州立大学）；Khoury College of Computer Science, Roux Institute at Northeastern University（计算机科学学院，东北大学罗克斯研究所）
Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases
Austria Institute of Technology（奥地利技术研究所）；Queen Mary University of London（伦敦玛丽女王大学）；University of Lübeck（吕贝克大学）；Singapore Institute of Technology（新加坡理工学院）
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
Department of Psychiatry, Wonkwang University Hospital（精神医学科，Wonkwang大学医院）；RSC LAB, MODULABS（RSC实验室，MODULABS）；The State University of New York, Korea, Republic of Korea（纽约州立大学，韩国）；Department of Pediatrics, Seoul National University Bundang Hospital（儿科，首尔国立大学医院 Bundang 分院）；Department of Pediatrics, Seoul National University College of Medicine（儿科，首尔国立大学医学院）
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
KAIST AI（韩国科学技术院人工智能学院）；Department of AI, Kyungpook National University（庆北国立大学人工智能系）；SmartSound（SmartSound公司）；Dongguk University（东国大学）；MODULABS（MODULABS机构）
XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization
Universidad de Valladolid（巴利亚多利德大学）；Instituto de Neurociencias de Castilla y León (INCYL)（卡斯蒂利亚-莱昂神经科学研究所（INCYL））；Complejo Asistencial Universitario de Salamanca（萨拉曼卡大学医疗综合体）
A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Center for Digital Safety & Security, Austrian Institute of Technology（奥地利技术研究所数字安全与安保中心）；Amazon Alexa（亚马逊Alexa）；Nuffield Department of Clinical Neurosciences, University of Oxford（牛津大学纳菲尔德临床神经科学系）
Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms
Indian Institute of Science（印度科学学院）；Ramaiah Medical College Hospital（拉马亚医学院医院）；General Hospital, Hoskote（霍斯考特综合医院）；PSG Institute of Medical Sciences and Research（PSG医学科学与研究院）
Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection
Indian Institute of Science（印度科学学院）；Fraunhofer Institute of Integrated Circuits（弗劳恩霍夫集成电路研究所）；Ramaiah Medical College Hospital（拉马亚医学院医院）；General Hospital, Hoskote（霍斯科特综合医院）；PSG Institute of Medical Sciences and Research（PSG医学科学与研究学院）
