Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms discards transient acoustic events and clinical context; (ii) limited data availability, exacerbated by severe class imbalance. To bridge these gaps, we present Resp-Agent, an autonomous multimodal system orchestrated by a novel Active Adversarial Curriculum Agent (Thinker-A$^2$CA). Unlike static pipelines, Thinker-A$^2$CA serves as a central controller that actively identifies diagnostic weaknesses and schedules targeted synthesis in a closed loop. To address the representation gap, we introduce a modality-weaving Diagnoser that weaves clinical text with audio tokens via strategic global attention and sparse audio anchors, capturing both long-range clinical context and millisecond-level transients. To address the data gap, we design a flow matching Generator that adapts a text-only Large Language Model (LLM) via modality injection, decoupling pathological content from acoustic style to synthesize hard-to-diagnose samples. As a foundation for this work, we introduce Resp-229k, a benchmark corpus of 229k recordings paired with LLM-distilled clinical narratives. Extensive experiments demonstrate that Resp-Agent consistently outperforms prior approaches across diverse evaluation settings, improving diagnostic robustness under data scarcity and long-tailed class imbalance. Our code and data are available at https://github.com/zpforlove/Resp-Agent.
The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respira…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use dee…
This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an accuracy of $87.04\%$, validating the hypothesis that one can detect RI through speech. Here, we study how Transformer neural network architectures can improve the performance on RI detection. This approach enables construction of an acoustic model. By choosing the correct pretraini…
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）
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）
UAEU（阿拉伯联合酋长国大学）；Staffordshire University（斯塔福德郡大学）；Tianjin University of Technology（天津理工大学）
Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Scien…
The recording of respiratory sounds was of significant benefit in the diagnosis of abnormalities in respiratory sounds. The duration of the sounds used in the diagnosis affects the speed of the diagnosis. In this study, the effect of window size on diagnosis of abnormalities in respiratory sounds and the most efficient recording time for diagnosis were analyzed. First, window size was applied to each sound in the data set consisting of normal and abnormal breathing sounds, 0.5 second and from 1 to 20 seconds Increased by 1 second. Then, the data applied to window size was inferred feature extr…
The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightn…
The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose …
Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers
Luca Healthcare（卢卡医疗）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Disease（上海市呼吸道传染病应急防控诊治重点实验室）
University of Cambridge（剑桥大学）；Alan Turing Institute（艾伦·图灵研究所）
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from digital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice …
Donghua University（东华大学）；University of Catania（卡塔尼亚大学）；PIEAS（巴基斯坦工程与应用科学学院）；Islamia University of Bahawalpur（巴哈瓦尔布尔伊斯兰大学）
With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively des…
In this study, a machine learning model was developed for automatically detecting respiratory system sounds such as sneezing and coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the "Pfizer digital medicine challenge" on the "OSFHOME" open access platform. "Environmental sound classification" called ESC-50 and AudioSet sound files were used to prepare the data…
Singapore University of Technology and Design（新加坡科技设计大学）
Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician's diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The…
Respiratory diseases are among the most common causes of severe illness and death worldwide. Prevention and early diagnosis are essential to limit or even reverse the trend that characterizes the diffusion of such diseases. In this regard, the development of advanced computational tools for the analysis of respiratory auscultation sounds can become a game changer for detecting disease-related anomalies, or diseases themselves. In this work, we propose a novel learning framework for respiratory auscultation sound data. Our approach combines state-of-the-art feature extraction techniques and adv…
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 adventiti…
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）
The COVID-19 pandemic has accelerated research on design of alternative, quick and effective COVID-19 diagnosis approaches. In this paper, we describe the Coswara tool, a website application designed to enable COVID-19 detection by analysing respiratory sound samples and health symptoms. A user using this service can log into a website using any device connected to the internet, provide there current health symptom information and record few sound sampled corresponding to breathing, cough, and speech. Within a minute of analysis of this information on a cloud server the website tool will outpu…
University of Kent（肯特大学）；University of Science and Technology of China（中国科学技术大学）；University of Oxford（牛津大学）；Graz University of Technology（格拉茨理工大学）
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this pa…
University of Oklahoma（俄克拉荷马大学）；AI4Lyf LLC（AI4Lyf有限责任公司）；Kharkiv National Medical University（哈尔科夫国立医科大学）；Michigan State University（密歇根州立大学）；Allergy, Asthma & Immunology Center PC（过敏、哮喘与免疫学中心PC）
Background: The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over th…
Optimising MFCC parameters for the automatic detection of respiratory diseases
Institute of Data Science, Maastricht University（数据科学研究所，马斯特里赫特大学）；Department of Respiratory Medicine, NUTRIM Research Institute of Nutrition and Translational Research in Metabolism, Faculty of Health Medicine and Life Sciences, Maastricht University（呼吸科系，营养与代谢转化研究 institute，健康医学与生命科学学院，马斯特里赫特大学）；Department of Respiratory Medicine, Maastricht University Medical Centre（呼吸科系，马斯特里赫特大学医学中心）；Department of Research and Development, Ciro（研发部，Ciro）
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）
This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases. Our experiments, conducted over the ICBHI benchmark meta-dataset of respiratory sound, achieve competitive I…
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）
BUET（孟加拉国工程技术大学）；Digital Healthcare Solutions（数字医疗解决方案公司）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
With the widespread use of telemedicine services, automatic assessment of health conditions via telephone speech can significantly impact public health. This work summarizes our preliminary findings on automatic detection of respiratory distress using well-known acoustic and prosodic features. Speech samples are collected from de-identified telemedicine phonecalls from a healthcare provider in Bangladesh. The recordings include conversational speech samples of patients talking to doctors showing mild or severe respiratory distress or asthma symptoms. We hypothesize that respiratory distress ma…
University of Kent（肯特大学）；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）
This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound…
Ho Chi Minh City University of Technology（胡志明市理工大学）；Da Nang University of Science and Technology（岘港科技大学）
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 res…
Recognizing patterns in lung sounds is crucial to detecting and monitoring respiratory diseases. Current techniques for analyzing respiratory sounds demand domain experts and are subject to interpretation. Hence an accurate and automatic respiratory sound classification system is desired. In this work, we took a data-driven approach to classify abnormal lung sounds. We compared the performance using three different feature extraction techniques, which are short-time Fourier transformation (STFT), Mel spectrograms, and Wav2vec, as well as three different classifiers, including pre-trained ResNe…
Developing a reliable sound detection and recognition system offers many benefits and has many useful applications in different industries. This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. Some methods which have been employed such as independent component analysis and blind source separation are examined. Finally, an examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.
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）
The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to r…
COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voice data recordings spontaneously provided by the general population (voice recordings and a short questionnaire) via their personal devices. The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. …
University of Coimbra（科英布拉大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；“G. Papanikolaou” General Hospital（“G.帕帕尼古拉乌”综合医院）
Mechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in …
Monash University（莫纳什大学）；Monash Children’s Hospital（莫纳什儿童医院）；BC Children’s Hospital Research Institute（不列颠哥伦比亚省儿童医院研究所）
Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random unders…
Imperial College London（帝国理工学院）；Griffith University（格里菲斯大学）；University of Augsburg（奥格斯堡大学）
Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability of classification results, especially ones based on deep learning, many explanation methods have been proposed using prototypes. However, existing explanation techniques often assume that the data is non-biased and the prediction results can be explained by a set of prototypical examples. In this work, we develop a unified example-based explanation method for selecting both representative data (prototypes) and out…
