In the framework of assessing the pathology severity in chronic cough diseases, medical literature underlines the lack of tools for allowing the automatic, objective and reliable detection of cough events. This paper describes a system based on two microphones which we developed for this purpose. The proposed approach relies on a large variety of audio descriptors, an efficient algorithm of feature selection based on their mutual information and the use of artificial neural networks. First, the possible use of a contact microphone (placed on the patient's thorax or trachea) in complement to the audio signal is investigated. This study underlines that this contact microphone suffers from reliability issues, and conveys little new relevant information compared to the audio modality. Secondly, the proposed audio-only approach is compared to a commercially available system using four sensors on a database with different sound categories often misdetected as coughs, and produced in various conditions. With average sensitivity and specificity of 94.7% and 95% respectively, the proposed method achieves better cough detection performance than the commercial system.
Technological University Dublin（都柏林理工大学）；University College Dublin（都柏林大学学院）
In this paper, an algorithm designed to detect characteristic cough events in audio recordings is presented, significantly reducing the time required for manual counting. Using time-frequency representations and independent subspace analysis (ISA), sound events that exhibit characteristics of coughs are automatically detected, producing a summary of the events detected. Using a dataset created from publicly available audio recordings, this algorithm has been tested on a variety of synthesized audio scenarios representative of those likely to be encountered by subjects undergoing an ambulatory …
Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning (ML) to provide widespread COVID-19 screening. However, there is currently no validated database of cough sounds with which to train such ML models. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses. First, we filtered the dataset using our open-sourced cough detection algorithm. Second, experienced …
University of Mons（蒙斯大学）；Faculty of Engineering, University of Mons（蒙斯大学工程学院）
This paper addresses the issue of cough detection using only audio recordings, with the ultimate goal of quantifying and qualifying the degree of pathology for patients suffering from respiratory diseases, notably mucoviscidosis. A large set of audio features describing various aspects of the audio signal is proposed. These features are assessed in two steps. First, their intrisic potential and redundancy are evaluated using mutual information-based measures. Secondly, their efficiency is confirmed relying on three classifiers: Artificial Neural Network, Gaussian Mixture Model and Support Vect…
Secretaría de Innovavión y Transformación Digital（创新与数字化转型秘书处）；Subsecretaría de Planificación Sanitaria（卫生规划副秘书处）；Ministerio de Salud（卫生部）
OBJECTIVE: Our objective is to evaluate the possibility of using cough audio recordings (spontaneous or simulated) to detect sound patterns in people who are diagnosed with COVID-19. The research question that led our work was: what is the sensitivity and specificity of a machine learning based COVID-19 cough classifier, using RT-PCR tests as gold standard? SETTING: The audio samples that were collected for this study belong to individuals who were swabbed in the City of Buenos Aires in 20 public and 1 private facilities where RT-PCR studies were carried out on patients suspected of COVID, and…
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. LR, SVM and MLP are evaluated as…
Imperial College London（伦敦帝国理工学院）；University of Augsburg（奥格斯堡大学）；North Bristol NHS Trust（北布里斯托尔国民保健服务信托基金）
Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability, minimal cost and improving performance as more data becomes available. This could enable regular COVID-19 testing at apopulation scale; (III) We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation; (IV) We release…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Augmented listening devices such as hearing aids often perform poorly in noisy and reverberant environments with many competing sound sources. Large distributed microphone arrays can improve performance, but data from remote microphones often cannot be used for delay-constrained real-time processing. We present a cooperative audio source separation and enhancement system that leverages wearable listening devices and other microphone arrays spread around a room. The full distributed array is used to separate sound sources and estimate their statistics. Each listening device uses these statistic…
Korea Advanced Institute of Science and Technology（韩国高等科学技术院）；SM Instruments Inc.（SM仪器公司）
Coughing is a typical symptom of COVID-19. To detect and localize coughing sounds remotely, a convolutional neural network (CNN) based deep learning model was developed in this work and integrated with a sound camera for the visualization of the cough sounds. The cough detection model is a binary classifier of which the input is a two second acoustic feature and the output is one of two inferences (Cough or Others). Data augmentation was performed on the collected audio files to alleviate class imbalance and reflect various background noises in practical environments. For effective featuring o…
Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphone per student group without any access to previous large datasets for training. This dissertation proposes a method of speaker identification using cross-correlation patterns associated to an array of virtual microphones, centered around the physical microphone. The virtual micro…
Tsinghua University（清华大学）；University of Washington（华盛顿大学）
Cough monitoring can enable new individual pulmonary health applications. Subject cough event detection is the foundation for continuous cough monitoring. Recently, the rapid growth in smart hearables has opened new opportunities for such needs. This paper proposes EarCough, which enables continuous subject cough event detection on edge computing hearables by leveraging the always-on active noise cancellation (ANC) microphones. Specifically, we proposed a lightweight end-to-end neural network model -- EarCoughNet. To evaluate the effectiveness of our method, we constructed a synchronous motion…
Cough sounds act as an important indicator of an individual's physical health, often used by medical professionals in diagnosing a patient's ailments. In recent years progress has been made in the area of automatically detecting cough events and, in certain cases, automatically identifying the ailment associated with a particular cough sound. Ethical and sensitivity issues associated with audio recordings of coughs makes it more difficult for this data to be made publicly available. However, without the public availability of a reliable database of cough sounds, developments in the area of aud…
Imperial College London（伦敦帝国理工学院）；North Bristol NHS Trust（北布里斯托尔国民健康服务体系信托基金）；University of Augsburg（奥格斯堡大学）
We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-positive or COVID-negative based on coughing and breathing audio recordings from a published crowdsourced dataset. In the current study, we demonstrate the potential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Cough and Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA. C…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Augmented listening devices, such as hearing aids and augmented reality headsets, enhance human perception by changing the sounds that we hear. Microphone arrays can improve the performance of listening systems in noisy environments, but most array-based listening systems are designed to isolate a single sound source from a mixture. This work considers a source-remixing filter that alters the relative level of each source independently. Remixing rather than separating sounds can help to improve perceptual transparency: it causes less distortion to the signal spectrum and especially to the inte…
University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics could lead to more timely treatment. The primary objective of this study is to explore the potential of longitudinal audio sam…
University of Mons（蒙斯大学）；Cliniques St Luc（圣卢克诊所）；University of Louvain（鲁汶大学）
The development of a system for the automatic, objective and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, acceler…
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；National University Health System, National University of Singapore（新加坡国立大学国立大学医疗系统）；National University Hospital, Singapore（新加坡国立大学医院）；Yong Loo Lin Medical School, National University of Singapore（新加坡国立大学杨潞龄医学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions f…
University of Stellenbosch（斯泰伦博斯大学）；TASK Applied Science（TASK应用科学公司）
We have performed cough detection based on measurements from an accelerometer attached to the patient's bed. This form of monitoring is less intrusive than body-attached accelerometer sensors, and sidesteps privacy concerns encountered when using audio for cough detection. For our experiments, we have compiled a manually-annotated dataset containing the acceleration signals of approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. As classifiers, we have considered convolutional neural networks (CNN), long-short-term-memory (LSTM) networks, an…
Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata
Hyfe Inc. U.S.A（Hyfe公司）；ISGlobal, Barcelona Institute for Global Health Spain（ISGlobal）；University of Washington, Department of Global Health U.S.A（华盛顿大学全球健康学院）
The present work proposes a deep-learning-based approach for the classification of COVID-19 coughs from non-COVID-19 coughs and that can be used as a low-resource-based tool for early detection of the onset of such respiratory diseases. The proposed system uses the ResNet-50 architecture, a popularly known Convolutional Neural Network (CNN) for image recognition tasks, fed with the log-Mel spectrums of the audio data to discriminate between the two types of coughs. For the training and validation of the proposed deep learning model, this work utilizes the Track-1 dataset provided by the DiCOVA…
Virufy AI Research Group（Virufy AI研究组）；University of Alexandria（亚历山大大学）；Stanford University（斯坦福大学）；Duke University（杜克大学）
Rapid and affordable methods of testing for COVID-19 infections are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed. Current approaches of detecting COVID-19 require in-person testing with expensive kits that are not always easily accessible. This study demonstrates that crowdsourced cough audio samples recorded and acquired on smartphones from around the world can be used to develop an AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1% (75.2%-78.3%). Furthermore, we show that our method is able to generalize to cro…
Stellenbosch University（斯泰伦博斯大学）；SAMRC Centre for Tuberculosis Research（南非医学研究理事会结核病研究中心）
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware. We use datasets that contain recordings of coughing, sneezing, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently eith…
Nokia Bell Labs（诺基亚贝尔实验室）；University College London（伦敦大学学院）；University of Oxford（牛津大学）
Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world. Besides many environmental dynamics, a primary factor that impacts the robustness of audio models is microphone variability. In this work, we propose Mic2Mic -- a machine-learned system component -- which resides in the inference pipeline of audio models and at real-time reduces the variability in audio data caused by m…
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Washington（华盛顿大学）；Harman International（哈曼国际）
Voice communication using the air conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super resolution algorithms can recover the high frequency loss to achieve high-fidelity audio, they require considerably more computational resources than available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super resolution system for BCM. To acco…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
We present an open-access dataset of over 8000 acoustic impulse from 160 microphones spread across the body and affixed to wearable accessories. The data can be used to evaluate audio capture and array processing systems using wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and clothing. We analyze the acoustic transfer functions of different parts of the body, measure the effects of clothing worn over microphones, compare measurements from a live human subject to those from a mannequin, and simulate the noise-reduction performance of several beamformers. The results su…
A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions. In this work, we propose a method to perform one-shot microphone style transfer. Given only a few seconds of audio recorded by a target device, MicAugment identifies the transformations associated to the input acquisition pipeline and uses the learned transformations to synthesize audio as if it were recorded under the same conditions as the target audio. We show that our method can successfully apply the style transfer …
This paper presents a complete hardware and software pipeline for real-time speech enhancement in noisy and reverberant conditions. The device consists of a microphone array and a camera mounted on eyeglasses, connected to an embedded system that enhances speech and plays back the audio in headphones, with a latency of maximum 120 msec. The proposed approach relies on face detection, tracking and verification to enhance the speech of a target speaker using a beamformer and a postfiltering neural network. Results demonstrate the feasibility of the approach, and opens the door to the exploration…
Ambisonics is an established framework to capture, process, and reproduce spatial sound fields based on its spherical harmonics representation. We propose a generalization of conventional spherical ambisonics to the spheroidal coordinate system and spheroidal microphone arrays, which represent sound fields by means of spheroidal wave functions. This framework is referred to as spheroidal ambisonics and a formulation for the case of prolate spheroidal coordinates is presented. Spheroidal ambisonics allows analytical encoding of sound fields using spheroidal microphone arrays. In addition, an an…
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
Northwestern University（西北大学）；Samsung Research America（三星美国研究院）
Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW…
MIT AutoID Laboratory（麻省理工学院自动识别实验室）；Harvard University（哈佛大学）；University of Edinburgh（爱丁堡大学）；University of Central Florida（中佛罗里达大学）；University of Milano-Bicocca（米兰比可卡大学）
We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the methodol…
Listening broadband physical model for microphones: a first step
Institut d’esthétique, des arts et technologies - UMR 8153 (CNRS/Université Paris 1/MENRT), Université Paris 1（巴黎第一大学美学与艺术技术研究所（UMR 8153））；ENS Louis-Lumière（国立高等路易·卢米埃尔学校）
University of California San Diego（加州大学圣迭戈分校）；Université de Sherbrooke（舍布鲁克大学）
We present a system for localizing sound sources in a room with several ad-hoc microphone arrays. Each circular array performs direction of arrival (DOA) estimation independently using commercial software. The DOAs are fed to a fusion center, concatenated, and used to perform the localization based on two proposed methods, which require only few labeled source locations (anchor points) for training. The first proposed method is based on principal component analysis (PCA) of the observed DOA and does not require any knowledge of anchor points. The array cluster can then perform localization on …
This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested agains…
Ton Duc Thang University（孙德胜大学）；ASICLAND（ASICLAND公司）；Sejong University（世宗大学）；Hanoi University of Industry（河内工业大学）；Northeastern University（东北大学）；Deakin University（迪肯大学）
SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in December 2019. The deadly virus has spread across the world, taking part in the global pandemic disease since March 2020. In addition, a recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths over the world. Therefore, it is vital to possess a self-testing service of SARS-CoV-2 at home. In this study, we introduce Fruit-CoV, a two-stage vision framework, which is capable of detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, we conv…
