Lung sounds refer to the sound generated by air moving through the respiratory system. These sounds, as most biomedical signals, are non-linear and non-stationary. A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound. In this paper, several approaches for classifying between no-crackle and crackle lung sounds are explored. Decomposition methods such as Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, and Discrete Wavelet Transform are used along with several feature extraction techniques like Principal Component Analysis and Autoencoder, to explore how various classifiers perform for the given task. An open-source dataset downloaded from Kaggle, containing chest auscultation of varying quality is used to determine the results of using the different decomposition and feature extraction combinations. It is found that when higher-order statistical and spectral features along with the Mel-frequency cepstral coefficients are fed to the classier we get the best performance with the kNN classifier giving the best accuracy. Furthermore, it is also demonstrated that using a combination of feature selection methods one can significantly reduce the number of input features without adversely affecting the accuracy of the classifiers.
Institute of Artificial Intelligence, Philipps-University Marburg（马尔堡大学人工智能研究所）；Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel（巴塞尔大学医院口腔颌面外科诊所）；Division of Respiratory and Critical Care Medicine, Philipps-University, Marburg（马尔堡大学呼吸与重症医学科）；Division of Internal Medicine, University Hospital Basel（巴塞尔大学医院内科）；Department of Gastroenterology, Endocrinology, Metabolism and Infectiology, Philipps-University, Marburg（马尔堡大学消化内科、内分泌科、代谢与感染病科）；Cardiology Department, University Hospital Gießen and Marburg（吉森和马尔堡大学医院心脏病科）；Department of Cardiology, University Heart and Vascular Center Hamburg（汉堡大学心脏和血管中心心脏病科）
Objective: This work proposes a semi-supervised training approach for detecting lung and heart sounds simultaneously with only one trained model and in invariance to the auscultation point. Methods: We use open-access data from the 2016 Physionet/CinC Challenge, the 2022 George Moody Challenge, and from the lung sound database HF_V1. We first train specialist single-task models using foreground ground truth (GT) labels from different auscultation databases to identify background sound events in the respective lung and heart auscultation databases. The pseudo-labels generated in this way were c…
In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the rec…
Large annotated lung sound databases are publicly available and might be used to train algorithms for diagnosis systems. However, it might be a challenge to develop a well-performing algorithm for small non-public data, which have only a few subjects and show differences in recording devices and setup. In this paper, we use transfer learning to tackle the mismatch of the recording setup. This allows us to transfer knowledge from one dataset to another dataset for crackle detection in lung sounds. In particular, a single input convolutional neural network (CNN) model is pre-trained on a source …
A Dual-Purpose Deep Learning Model for Auscultated Lung and Tracheal Sound Analysis Based on Mixed Set Training
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（远东纪念医院）；Heroic Faith Medical Science Co., Ltd.（鸿信医学科技股份有限公司）；En Chu Kong Hospital（恩主公医院）；Oriental Institute of Technology（东方科技大学）；Avalanche Computing Inc.（雪崩计算公司）；Academia Sinica（中央研究院）；National Taiwan University Hospital（台湾大学医院）
Lung auscultation is the most effective and indispensable method for diagnosing various respiratory disorders by using the sounds from the airways during inspirium and exhalation using a stethoscope. In this study, the statistical features are calculated from intrinsic mode functions that are extracted by applying the HilbertHuang Transform to the lung sounds from 12 different auscultation regions on the chest and back. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN). The DBN classifier model with two hidden layers has been t…
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）
A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalati…
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（亚东纪念医院）；Heroic Faith Medical Science Co., Ltd.（Heroic Faith Medical Science 有限公司）；Avalanche Computing Inc.（Avalanche Computing 公司）；Academia Sinica（中央研究院）；HCC Healthcare Group（HCC 医疗集团）
Purpose: We previously established an open-access lung sound database, HF_Lung_V1, and developed deep learning models for inhalation, exhalation, continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS) detection. The amount of data used for training contributes to model accuracy. Herein, we collected larger quantities of data to further improve model performance. Moreover, the issues of noisy labels and sound overlapping were explored. Methods: HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.45x increase in the number of audio files. Convolutional neural network-bidirect…
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…
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in enabling tele-screening of fatal lung diseases. Deep neural networks (DNNs) have shown a lot of promise for such problems, and are an obvious choice. However, DNNs are extremely data hungry, and the largest respiratory dataset ICBHI has only 6898 breathing cycles, which is still small for training a satisfactory DNN model. In this work, RespireNet, we propose a simple CNN-based model, along with a suite of novel tec…
Monash University（莫纳什大学）；Shiraz University（设拉子大学）；Emory University（埃默里大学）；Monash Children’s Hospital（莫纳什儿童医院）；University British Columbia（不列颠哥伦比亚大学）；BC Children’s Hospital Research Institute（不列颠哥伦比亚儿童医院研究所）
Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess these methods and compare with existing single-source separation methods, an artificial mixture dataset was generated comprising of heart, lung and noise sounds. Signal-to-noise ratios were then calculated for these artificial mixtures. T…
Seoul National University of Science and Technology（首尔科学技术大学）
Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesion-Separation Loss (PCSL) and Global Patient Alignment Loss (GPAL). PCSL clusters features of the same patient while separating those from other patients to capture patient variability, whereas GPAL draws each patient's centroid toward a gl…
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…
Obtaining high-quality heart and lung sounds enables clinicians to accurately assess a newborn's cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation-based approach is proposed to separate noisy chest sound recordings into heart, lung, and noise components to address this problem. This method is achieved through training with 20 high-quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second…
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（亚东纪念医院）；Heroic Faith Medical Science Co., Ltd.（英雄信仰医学科技股份有限公司）；Avalanche Computing Inc.（雪崩计算公司）；Academia Sinica（中央研究院）
We previously established a large lung sound database, HF_Lung_V2 (Lung_V2). We trained convolutional-bidirectional gated recurrent unit (CNN-BiGRU) networks for detecting inhalation, exhalation, continuous adventitious sound (CAS) and discontinuous adventitious sound at the recording level on the basis of Lung_V2. However, the performance of CAS detection was poor due to many reasons, one of which is the highly diversified CAS patterns. To make the original CNN-BiGRU model learn the CAS patterns more effectively and not cause too much computing burden, three strategies involving minimal modif…
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…
Queensland University of Technology（昆士兰科技大学）；M3DICINE Pty Ltd（M3DICINE私人有限公司）
This paper proposes a novel framework for lung sound event detection, segmenting continuous lung sound recordings into discrete events and performing recognition on each event. Exploiting the lightweight nature of Temporal Convolution Networks (TCNs) and their superior results compared to their recurrent counterparts, we propose a lightweight, yet robust, and completely interpretable framework for lung sound event detection. We propose the use of a multi-branch TCN architecture and exploit a novel fusion strategy to combine the resultant features from these branches. This not only allows the n…
Monash University（莫纳什大学）；Emory University（埃默里大学）；University of British Columbia（不列颠哥伦比亚大学）
Digital stethoscopes in combination with telehealth allow chest sounds to be easily collected and transmitted for remote monitoring and diagnosis. Chest sounds contain important information about a newborn's cardio-respiratory health. However, low-quality recordings complicate the remote monitoring and diagnosis. In this study, a new method is proposed to objectively and automatically assess heart and lung signal quality on a 5-level scale in real-time and to assess the effect of signal quality on vital sign estimation. For the evaluation, a total of 207 10s long chest sounds were taken from 1…
Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds. It can be realized using a neural network that extracts the target sound conditioned on a 1-hot vector that represents the desired AE class. With this approach, embedding vectors associated with the AE classes are directly optimized for the extraction of sound classes seen during training. However, it is not easy to extend this framework to new AE classes, i.e. unseen during training. Recently, speech, music, or AE sound extraction based on enrollment audio of the desired…
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）
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…
In recent years, many innovative solutions for recording and viewing sounds from a stethoscope have become available. However, to fully utilize such devices, there is a need for an automated approach for detecting abnormal lung sounds, which is better than the existing methods that typically have been developed and evaluated using a small and non-diverse dataset. We propose a machine learning based approach for detecting crackles in lung sounds recorded using a stethoscope in a large health survey. Our method is trained and evaluated using 209 files with crackles classified by expert listeners…
Target sound extraction (TSE) aims to extract the sound part of a target sound event class from a mixture audio with multiple sound events. The previous works mainly focus on the problems of weakly-labelled data, jointly learning and new classes, however, no one cares about the onset and offset times of the target sound event, which has been emphasized in the auditory scene analysis. In this paper, we study to utilize such timestamp information to help extract the target sound via a target sound detection network and a target-weighted time-frequency loss function. More specifically, we use the…
TSE-PI: Target Sound Extraction under Reverberant Environments with Pitch Information
Peking University（北京大学）；National Key Laboratory of General Artificial Intelligence（通用人工智能全国重点实验室）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the audio signal of the sounds of target SE classes in a mixture of sounds while suppressing all other sounds. We can achieve this with a neural network that extracts the target SEs by conditioning it on clues representing the target SE classes. Two types of clues have been proposed, i.e., target SE class labels and enrollment audio samples (or audio queries), which are pre-recorded audio samples of sounds from the target…
Patient-level validation of a foundation-model pipeline for pediatric lung sounds: physician-annotated adventitious events are recognized, disease groups are not reliably predicted
Department of Pediatric Intensive Care Medicine, Life Support Center, Hacettepe University（儿科重症医学科、生命支持中心、哈切塔佩大学）；Department of Pediatric Emergency Medicine, Life Support Center, Hacettepe University Faculty of Medicine（儿科急诊医学科、生命支持中心、哈切塔佩大学医学院）
Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture of sources. It often uses a model conditioned on a fixed form of target sound clues, such as a sound class label, which limits the ways in which users can interact with the model to specify the target sounds. To leverage variable number of clues cross modalities available in the inference phase, including a video, a sound event class, and a text caption, we propose a unified transformer-based TSE model architecture, w…
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. By this method, we estimate a time-frequency mask from an input mixture spectrogram and an onomatopoeic word using a U-Net architecture, then extract the corresponding target sound by masking the spectrogram. Experimental results indicate that the proposed method can extract only the target sound cor…
In this work, we propose to extend a state-of-the-art multi-source localization system based on a convolutional recurrent neural network and Ambisonics signals. We significantly improve the performance of the baseline network by changing the layout between convolutional and pooling layers. We propose several configurations with more convolutional layers and smaller pooling sizes in-between, so that less information is lost across the layers, leading to a better feature extraction. In parallel, we test the system's ability to localize up to 3 sources, in which case the improved feature extracti…
Cross-attention Inspired Selective State Space Models for Target Sound Extraction
National Key Laboratory of General Artificial Intelligence（国家级通用人工智能重点实验室）；School of Intelligence Science and Technology（智能科学与技术学院）；Peking University（北京大学）
UMONS（蒙斯大学）；Acapela Group S.A.（Acapela集团股份有限公司）；GIPSA-lab（GIPSA实验室）；CNRS（法国国家科学研究中心）；INPG（格勒诺布尔国立理工学院）；Univ. Stendhal（司汤达大学）；UJF（约瑟夫·傅里叶大学）
The problem of pitch tracking has been extensively studied in the speech research community. The goal of this paper is to investigate how these techniques should be adapted to singing voice analysis, and to provide a comparative evaluation of the most representative state-of-the-art approaches. This study is carried out on a large database of annotated singing sounds with aligned EGG recordings, comprising a variety of singer categories and singing exercises. The algorithmic performance is assessed according to the ability to detect voicing boundaries and to accurately estimate pitch contour. …
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 University of Tokyo（东京大学）；National Institute of Technology, Kagawa College（国立高等专门学校机构 香川高等专门学校）；Yamaha Corporation（雅马哈公司）
A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us to flexibly edit sounds by changing the fundamental frequency, timbre feature, and loudness (synthesis parameters) extracted from an input sound. However, it is designed for a monophonic harmonic sound and cannot handle mixtures of harmonic sounds. In this paper, we propose a model (DDSP mixture model) that represents a mixture as the sum of the outputs of multiple pretrained DDSP autoencoders. By fitting the output…
Language-Queried Target Sound Extraction Without Parallel Training Data
School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；North Carolina State University（北卡罗来纳州立大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；Key Laboratory of System and Control, AMSS, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统与控制重点实验室）；The Chinese University of Hong Kong（香港中文大学）
Indian Institute of Technology, Kanpur（印度理工学院坎普尔分校）
Melody estimation or melody extraction refers to the extraction of the primary or fundamental dominant frequency in a melody. This sequence of frequencies obtained represents the pitch of the dominant melodic line from recorded music audio signals. The music signal may be monophonic or polyphonic. The melody extraction problem from audio signals gets complicated when we start dealing with polyphonic audio data. This is because in generalized audio signals,the sounds are highly correlated over both frequency and time domains. This complex overlap of many sounds, makes identification of predomin…
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）
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；WS Audiology（WS听力集团）
A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommodating a particularly intuitive explanation. The first step consists of signal-adaptive rotations applied on the input stereo scene, which translate the primary sound sources into the center of the rotated scene. The second step applies a center-channel extraction method, based on a simple signal model and optimal in the mean-squared-error sense. The performance is evaluated by using the estimated ambient component to enable surround…
