# Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification Using Deep Learning

Fabien Mouomene Moffo, Auguste Vigny Noumsi Woguia, Joseph Mvogo Ngono, Samuel Bowong

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Fabien Mouomene Moffo, Auguste Vigny Noumsi Woguia, Joseph Mvogo Ngono, Samuel Bowong.

Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification

Using Deep Learning. 2024. hal-04528741

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# Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification Using Deep Learning

Fabien Mouomene Moffo, Auguste Vigny Noumsi Woguia, Joseph Mvogo Ngono, Samuel Bowong

<figure><img src="image_1.png" /></figure>

## To cite this version:

Fabien Mouomene Moffo, Auguste Vigny Noumsi Woguia, Joseph Mvogo Ngono, Samuel Bowong. Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification Using Deep Learning. Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, 2025, Volume 42 - Numéro spécial CRI 2023 - 2024, 10.46298/arima.13343. hal-04528741v3

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Submitted on 22 Nov 2024

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# Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification Using Deep Learning

Fabien Mouomene Moffo¹, Auguste Noumsi¹, Joseph Mvogo Ngono², Samuel Bowong¹

¹Douala University, Faculty of Science, Department of Mathematics and Computer Science, Cameroon

²Douala University, University Institute of Technology, Department of Computer Science, Cameroon

*E-mail: Fabien.Mouomene Moffo fmouomene@yahoo.fr

## Abstract

Cough is the most recurrent symptom of lung disease. Additionally, COVID-19 is an unprecedented respiratory illness. The COVID-19 pandemic has resulted in over 700 million positive cases and 7 million deaths worldwide, with statistics continuing to rise. An efficient, affordable, and widely available diagnostic tool is crucial in the fight against lung disease and the COVID-19 crisis. Deep learning and machine learning algorithms could be utilised to analyse the cough sounds of infected patients and make predictions. We use constructivist logic. The cough data are from our research lab and the COUGHVID research lab. This Diagnostic approach, based on deep learning and feature extraction from Mel spectrograms, recognise cough sounds from sick people without COVID-19, with severe and mild COVID-19, and also recognize cough sounds from healthy patients. The model used is a variant of ConvNet. During data acquisition for model training, it is important to consider quiet environments. This can reduce both systematic and random errors in audio quality. The architecture of the convolutional neural networks exploited, gives F1-score of 89% and accuracy of 90.33%. This system has the potential to make a significant positive impact on society by reducing virus transmission, expediting patient treatment, and freeing up hospital resources. Additionally, early detection of COVID-19 can prevent disease progression and enhance screening effectiveness.

## Keywords

COVID-19; Diagnosis; Cough; Deep learning; ConvNet

## I INTRODUCTION

Cough is the most recurrent symptom of lung disease. In addition, COVID-19 is an un- paralleled lung disease, and The COVID-19 pandemic has resulted in over 700 million positive cases and 7 million deaths worldwide, with statistics continuing to rise [27, 30]. The viruses re- sponsible for respiratory diseases are present in the respiratory secretions of patients, which can be projected as droplets around the patient during coughing [17]. These droplets are the main sources of contamination. COVID-19 contamination can be direct and indirect [12]. Direct during the patients cough and indirect after the patients cough through infected surfaces [21]. Medical and clinical solutions have been used to stop the spread of the disease, such as nucleic acid testing (RNA) and amalgam, polymerase chain reaction (PCR) testing. The diagnosis of Covid-19 is confirmed by RT-PCR testing of nasopharyngeal and oropharyngeal swabs. Test results are typically available within four hours. The samples collected for the initial detection
# Analysis of COVID-19 Coughs: From the Mildest to the Most Severe Form, a Realistic Classification Using Deep Learning

Fabien Mouoméne Moffo*¹, Auguste Noumsi¹, Joseph Mvogo Ngono², Samuel Bowong¹

¹Douala University, FS, Department of Mathematics and Computer Science, Cameroon

²Douala University, IUT, Department of Computer Science, Cameroon

*E-mail: Fabien MOUOMENE fmouomene@yahoo.fr

DOI: 10.46298/arima.13343

Submitted on 3 April 2024 - Published on 28 October 2024

Volume: 42 - Numéro spécial CRI 2023 - 2024 - Year: 2024

Special Issue: 42 - Numéro spécial CRI 2023 - 2024

Editors: Paulin Melatagia, René Ndoundam, Kamel Barkaoui, Blaise Omer Yenke

## Abstract

Cough is the most common symptom of lung disease. COVID-19, a respiratory illness, has caused over 700 million positive cases and 7 million deaths worldwide. An effective, affordable, and widely available diagnostic tool is crucial in combating lung disease and the COVID-19 pandemic. Deep learning and machine learning algorithms could be used to analyze the cough sounds of infected patients and make predictions. Our research lab and the COUGHVID research lab provide the cough data. This diagnostic approach can distinguish cough sounds from COVID-19 patients and people suffering from other ailments as well as healthy people using deep learning and feature extraction from Mel spectrograms. The model used is a variant of ConvNet. This ConvNet model can easily capture features in MFCC Vectors and enable convolution parallelism, which increases processing speed. ConvNet attains translational invariance in features through the sharing of weights between layers. During data acquisition for model training, it is important to consider quiet environments to reduce errors in audio quality. The architecture of the convolutional neural networks gives an F1-score of 89%, an accuracy of 90.33% and sensitivity of 87.3%. This system has the potential to significantly impact society by reducing virus transmission, expediting patient treatment, and freeing up hospital resources. Early detection of COVID-19 can prevent disease progression and enhance screening effectiveness.

## Keywords

Diagnosis; Cough; Deep learning; MFCC features; ConvNet

## I INTRODUCTION

Cough is the most common symptom of lung disease. COVID-19 is a significant lung disease, and the pandemic has resulted in over 700 million positive cases and 7 million deaths worldwide, with numbers still rising [30, 33]. The viruses responsible for respiratory diseases are present in the respiratory secretions of patients, which can be projected as droplets around the patient during coughing [18]. These droplets are the main sources of contamination.
of COVID-19 include nasopharyngeal swabs, lower respiratory tract swabs (sputum, BAL) in
cases with parenchymal involvement, and blood samples[29]. The interpretation of the RT-PCR
test carries significant weight due to its higher specificity and sensitivity. Other scientific meth-
ods in epidemiology are published for the prevention of covid-19 [13, 19].

Despite the reliability, RT-PCR tests for SARS-CoV-2 pose some problems in use. Require-
ment of health care personnel, a risk of contamination during collection, the high cost of testing
due to the need for expensive reagents and tools [23]. In addition, the processing of the collected
samples is not in real-time. These problems, so unpleasant, analogy, prevent RT-PCR tests from
being large-scale screening tools. Although some countries have achieved high vaccination
rates necessary for herd immunity, many continents affected by the COVID-19 pandemic still
have very low rates due to vaccine backlogs and failures. In the face of this controversy, experts
are uncertain about the achievement of herd immunity, especially with the unpredictability of
viral variants. Therefore, an effective, inexpensive, and ubiquitous diagnostic tool is essential
to address the lung disease and COVID-19 crisis.

One of the most common symptoms of COVID-19 is a dry cough, which is present in approxi-
mately 67.7% of cases [22]. Convolutional neural networks, a revolution in artificial intelligence
(AI), show promise in creating such a solution. Deep learning and machine learning algorithms
can be utilised to analyse the cough sounds of infected patients and make predictions. Deep
learning refers to the application of learning methods to networks or graphs of interconnected
parameterised modules[16]. The parameters of these modules are modified through learning us-
ing gradient descent, with the gradient typically obtained through backpropagation. An exam-
ple of deep learning is the training of multilayer neural networks. Research labs need to collect
sound recordings for COVID-19 patients of all ages, in changing environments, symptomatic or
asymptomatic. This data provides an opportunity for AI algorithms to learn pandemic-specific
patient audio patterns. Cough recordings are commonly collected to detect COVID-19 by lab-
oratory such as COUGHVID and COSWARA. However, the sound quality and format of this
recorded data are not always objective and can vary depending on the tool used and the envi-
ronment. Additionally, missing donor data can render some metadata unsuitable for learning
algorithms.

A deep learning network based on cough sounds is proposed in this study. The model, which is a variant of ConvNet, successfully learns from cough audio submissions and classifies them into four categories: severe and non-severe COVID-19 positive donors, healthy patients and other patients with lung diseases other than COVID-19.

The paper is structured as follows: Section I presents the materials and methods, while Section II presents the results and discussions.

## II MATERIALS AND METHODS

We are conducting a cross-sectional study with a descriptive, exploratory and diagnostic
approach. Our work has implications for the world's population. This research is being car-
ried out in our laboratory before the COVID-19 pandemic. In collaboration with healthcare
professionals, we collect cough samples from healthy patients and patients suffering from res-
piratory diseases. The dataset was later updated with data on patients who tested positive for
COVID-19. The participants' recordings are made in a quiet environment[9]. Any recording
with excessive ambient noise is excluded. Patients consented to participate in sound recordings
with the probity of the team. The subjects are geographically dispersed worldwide to ensure
sample diversity. This decision was made because lung disease and COVID-19 are not limited
to a single country or continent.
COVID-19 contamination can be direct or indirect [13]. Direct contamination occurs during the patient's cough, while indirect contamination can occur after the patient's cough through infected surfaces [23]. Medical and clinical solutions, such as nucleic acid testing (NAT) and polymerase chain reaction (PCR) testing, have been used to stop the spread of the disease. The diagnosis of COVID-19 is confirmed by RT-PCR testing of nasopharyngeal and oropharyngeal swabs. Test results are typically available within four hours. The samples collected for the initial detection of COVID-19 include nasopharyngeal swabs, lower respiratory tract swabs (sputum, BAL) in cases with parenchymal involvement, and blood samples (Respiratory Syndrome) [32]. The interpretation of the RT-PCR test carries significant weight due to its higher specificity and sensitivity. Other scientific methods in epidemiology have been published for the prevention of COVID-19 [14, 20].

However, RT-PCR tests for SARS-CoV-2 pose some challenges. These include the requirement of health care personnel, the risk of contamination during collection, and the high cost of testing due to the need for expensive reagents (viral RNA, and reverse transcriptase) and tools [26]. Reagents are defined as chemicals or substances employed in laboratory tests, such as those used for the detection of the SARS-CoV-2 virus, to facilitate the identification of the virus's presence in a sample obtained from a patient. These reagents are indispensable for the accurate and reliable diagnosis of the disease. In addition, the processing of the collected samples is not in real-time. These issues prevent RT-PCR tests from being used as large-scale screening tools. Although some countries have achieved high vaccination rates necessary for herd immunity, many continents affected by the COVID-19 pandemic still have very low rates due to vaccine backlogs and failures. Experts are uncertain about the achievement of herd immunity, especially with the unpredictability of viral variants. Therefore, an effective, inexpensive, and widely available diagnostic tool is essential to address the lung disease and COVID-19 crisis.
One of the most common symptoms of COVID-19 is a cough [10, 12, 25, 27]. Convolutional neural networks, a revolution in artificial intelligence (AI), show promise in creating a solution. Deep learning and machine learning algorithms can be used to analyze the cough sounds of infected patients and make predictions. Deep learning refers to the application of learning methods to networks or graphs of interconnected parameterized modules [17]. The parameters of these modules are modified through learning using gradient descent, with the gradient typically obtained through backpropagation. An example of deep learning is the training of multilayer neural networks. Research labs need to collect sound recordings for COVID-19 patients of all ages, in changing environments, symptomatic or asymptomatic. This data provides an opportunity for AI algorithms to learn pandemic-specific patient audio patterns. Cough recordings are commonly collected to detect COVID-19 by laboratories such as COUGHVID and COSWARA. However, the sound quality and format of this recorded data are not always objective and can vary depending on the tool used and the environment. Additionally, missing donor data can render some metadata unsuitable for learning algorithms.

In this study, a deep-learning network based on cough sounds is discussed. The model is a variant of ConvNet and has been successful in learning from cough audio submissions, categorizing them into four groups: severe and non-severe COVID-19-positive individuals, healthy patients, and patients with lung conditions unrelated to COVID-19.

The paper is structured as follows: Section I presents the materials and methods, while Section II presents the results and discussions.
Figure 1 shows the impact of the COVID-19 pandemic on populations: a geographical visualisation, this impact is marked by the color blue [28]. The map illustrates that COVID-19 is most prevalent in America, Asia, and Europe, with the most affected areas having an incidence rate of over 70 million cases. While there is a downward trend in COVID-19 infections in most regions, the disease remains a significant public health concern in many countries.

Figure 2 illustrates the methodology used in this study. The methodology of this study involves collecting data, preprocessing it using appropriate techniques, transforming it into a spectrum, and using the spectrum to train the CNN model.

<figure><img src="image_3.png" /><figcaption>Figure 1: The Geographical Overview of the Impact of the Covid-19 Pandemic on Populations.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2: Methodology of this Study.</figcaption></figure>

## 2.1 Data Collection

The data analyzed correspond to the audio recordings of coughing patients. These audio data, collected randomly, were recorded from the microphones of cell phones and computers. Our dataset, consisting of 3300 data, describes coughs of healthy patients, sick patients with negative COVID-19 test and patients with severe and not severe COVID-19. Samples from 258 healthy patients and sick patients with a negative COVID-19 test were recorded before the COVID-19 pandemic [18]. The remaining data during the pandemic through the COUGHVID
## II MATERIALS AND METHODS

We are conducting a cross-sectional study with a descriptive, exploratory and diagnostic approach. Our work has implications for the world's population. This research is being carried out in our laboratory before the COVID-19 pandemic. In collaboration with healthcare professionals, we collect cough samples from healthy patients and patients suffering from respiratory diseases. The dataset was later updated with data on patients who tested positive for COVID-19. The participants' recordings are made in a quiet environment [9]. Any recording with excessive ambient noise is excluded. Patients consented to participate in sound recordings with the probity of the team. The subjects are geographically dispersed worldwide to ensure sample diversity. The geographic dispersion of individuals or objects across a given geographic area is a measure of their spatial distribution. While statistical measures such as range, standard deviation, and interquartile range provide numerical insights, the visualization of this dispersion through maps and spatial analysis techniques offers a more intuitive and comprehensive understanding. This decision was made because lung disease and COVID-19 are not limited to a single country or continent.

Figure 1 shows the impact of the COVID-19 pandemic on populations: a geographical visualization, this impact is marked by the color blue [31]. The map illustrates that COVID-19 is most prevalent in America, Asia, and Europe, with the most affected areas having an incidence rate of over 70 million cases. While there is a downward trend in COVID-19 infections in most regions, the disease remains a significant public health concern in many countries.

Figure 2 illustrates the methodology used in this study. The methodology of this study involves collecting data, preprocessing it using appropriate techniques, transforming it into a spectrum, and using the spectrum to train the CNN model.

<figure><img src="image_3.png" /><figcaption>Figure 1: The Geographical Overview of the Impact of the COVID-19 Pandemic on Populations.</figcaption></figure>
laboratory are public data. The metadata, of the known patient population, indicates the patients condition, age, gender, and geographic location. Recurrent symptoms in COVID-19 are: cough, fever, and fatigue. High fever is the symptom that has been taken into account to most describe severe COVID-19 [1]. The in vivo severity of COVID-19 is due to the presence of hemoptysis, decreased white blood cells and renal failure [14, 20].

Figure 3 illustrates the geographical sources of all collected cough sounds. The histogram analysis indicates an approximately equal distribution of data sources across continents. socio-economic factors. The intercontinental connection facilitates the spread of the pandemic to all mentioned continents.

Figure 4 shows the Statistical Clinical forms of the dataset. Histogram Present The frequency of Healthy is much higher than the other frequencies, which can be explained by the fact that most people with respiratory illnesses cough and are sometimes isolated from their loved ones.

<figure><img src="image_5.png" /><figcaption>Figure 3: Geographical sources of the dataset collected.</figcaption></figure>

## 2.2 Preprocessing

First, the audio signal, like any other incoming signal, must be digitized into a sequence of numbers called samples, each of which represents the air pressure on the microphone at a given time. The raw data collected is in Ogg, Webm and Mp3 formats. We first developed an algorithm to automatically convert the data set from each compressing audio format to the wav format. The WAV format provides superior audio quality compared to Ogg and WebM formats, as it does not compress the audio file. The data is divided into four categories: samples of sick patients without COVID-19 (Sick), samples of healthy patients (Healthy), samples of patients with severe COVID-19 (Severe), and samples of patients with non-severe COVID-19 (Mild). Normalization to a frequency of 22 kHz was applied to samples with different frequencies. A second algorithm is developed to segment the audio samples into equal 3-second durations for input into the 2D CNN. The data set is resampled and normalised to resolve the problem of data imbalance. Figure 5 illustrates the nature of Waveform for each cough category. According to scientific literature, coughing is a three-phase motor act [3]. The first phase is the inspiratory
<figure><img src="image_4.png" /><figcaption>Figure 2: Methodology of this study.</figcaption></figure>

## 2.1 Data Collection

The analyzed data pertains to audio recordings of coughing patients. These audio recordings were collected randomly from the microphones of cell phones and computers. Our dataset comprises 3300 entries, describing coughs of healthy patients, patients with a negative COVID-19 test, and patients with severe and non-severe COVID-19. Samples from 258 healthy patients and patients with a negative COVID-19 test were recorded pre-pandemic. The remaining data, recorded during the pandemic through the COUGHVID laboratory, are public. The COUGHVID dataset provides over 20,000 crowdsourced cough recordings representing a wide range of subject ages, genders, geographic locations, and COVID-19 statuses [29]. Crowdsourcing enables the rapid and cost-effective collection of a substantial amount of data, as well as the utilisation of the expertise and diversity of contributors from various continents. COUGHVID issues calls for contributions via online platforms, which are then responded to by participants, who carry out tasks related to cough sound recording. Once the data has been collected, it is subjected to a verification and validation process by COUGHVID before being employed for research projects. The metadata provides the patients' condition, age, gender, and geographic location. Recurrent COVID-19 symptoms include cough, fever, and fatigue, with high fever being the most prominent symptom of severe cases[1, 19]. Severe COVID-19 is characterized by the presence of hemoptysis, decreased white blood cells, and renal failure [15, 22].

Figure 3 illustrates the geographical sources of all collected cough sounds, indicating an almost equal distribution across continents. Socio-economic factors and intercontinental travel are contributing factors to the pandemic's spread.

Figure 4 presents the statistical clinical forms of the dataset, showing a much higher frequency of healthy individuals, potentially due to respiratory illnesses leading to coughing and isolation from loved ones.

## 2.2 Preprocessing

First, the audio signal, like any other incoming signal, must be digitized into a sequence of numbers called samples, each of which represents the air pressure on the microphone at a given time. The raw data collected is in Ogg, Webm, and MP3 formats. We first developed an algorithm to automatically convert the data set from each compressing audio format to the WAV format. The WAV format provides superior audio quality compared to Ogg and WebM formats, as it does not compress the audio file. The data is divided into four categories: samples of sick patients without COVID-19 (Sick, 1000 cough sound data), samples of healthy patients (Healthy, 1340 cough sound data), samples of patients with severe COVID-19 (Severe, 280 cough sound data), and samples of patients with non-severe COVID-19 (Mild, 680 cough sound data). Normalization to a frequency of 22 kHz was applied to samples with different frequencies. A second algorithm is developed to segment the audio samples into equal 3-second
<figure><img src="image_6.png" /><figcaption>Figure 4: Statistics: Clinical forms of the dataset.</figcaption></figure>

phase, during which air is drawn into the lungs with the glottis fully open. The second phase is the compressive phase, where respiratory muscles contract and compress the air against the glottis with the glottis fully closed, increasing the pressure. Finally, during the expulsive phase, the glottis suddenly reopens and air is rapidly expelled, generating the cough sound.
Figure 5 presents an example of each waveform from one of the four cough categories studied. The waveforms of the Heath category have less important expulsive phases than the waveforms of the sick patients. The most important expulsive phases are those of the (Sick) and (Severe) COVID-19 categories.

## 2.3 Feature Extraction

Acoustic characteristics are extracted from the audio data using the log-scale mixing spectrogram method. This method enables the representation and differentiation of various sound events. Logarithmic spectrograms provide detailed acoustic information from short audio recordings. Sound events that occur within a short period of time can be efficiently detected. To derive the log-scale spectrogram of the original sounds, three steps are required. First, the sound clips must be converted from the time domain to the frequency domain by applying the short-time Fourier transform [8]. Then, a Hann window of 23 ms with a jump length of 256 is used. The audio sequence is transformed into a spectrogram with 120 graduated frequency bands and magnitudes that represent the characteristics of the human audible range, including the decibel scale. Each speech signal is represented by a sequence of vectors with dimensions 120x40, with one vector every 10 milliseconds, resulting in 100 vectors per second [2]. The convolutional network analyzes a window of 120 vectors, which represents sound and is equivalent to 120x40 pixels. It then classifies the elementary sound present in the middle of the window. Additionally, the derivative of the logarithmically scaled mixed spectrogram was used to provide extra channels for better understanding and distinguishing acoustic features.
Figure 6 shows a flowchart that describes the algorithm for converting cough audio signals
<figure><img src="image_5.png" /><figcaption>Figure 3: Geographical sources of the dataset collected.</figcaption></figure>

durations for input into the 2D CNN. The dataset is resampled and normalized in order to mitigate the challenge of data imbalance. Figure 5 illustrates the nature of Waveform for each cough category. According to scientific literature, coughing is a three-phase motor act [3]. The first phase is the inspiratory phase, during which air is drawn into the lungs with the glottis fully open. The second phase is the compressive phase, where respiratory muscles contract and compress the air against the glottis with the glottis fully closed, increasing the pressure. Finally, during the expulsive phase, the glottis suddenly reopens and air is rapidly expelled, generating the cough sound.

Figure 5 presents an example of each waveform from one of the four cough categories studied. The waveforms of the Heath category show fewer significant expulsive phases compared to those of the sick patients. The most significant expulsive phases are found in the (Sick) and (Severe) COVID-19 categories.

## 2.3 Feature Extraction

The audio data's acoustic characteristics are extracted using the log-scale mixing spectrogram method. This method allows for the representation and differentiation of various sound events. Logarithmic spectrograms offer detailed acoustic information from short audio recordings. It enables efficient detection of sound events occurring within a short period. In order to create the log-scale spectrogram of the original sounds, three steps are necessary. Firstly, the sound clips must be converted from the time domain to the frequency domain by applying the short-time Fourier transform. Then, a Hann window of 23 ms with a jump length of 256 is used [8]. The audio sequence is transformed into a spectrogram with 120 graduated frequency bands and magnitudes that represent the characteristics of the human audible range, including the decibel scale. Each speech signal is represented by a sequence of vectors with dimensions 120x40, with one vector every 10 milliseconds, resulting in 100 vectors per second [2]. The convolutional network analyzes a 120x40 window of vectors representing sound. It then classi-
<figure><img src="image_7.png" /><figcaption>Figure 5: Waveform for each Cough Category.</figcaption></figure>

into MFCC feature vectors [7]. The process of converting an audio signal into MFCC (Mel-Frequency Cepstral Coefficients) spectrum follows this path: The audio signal is divided into fixed-length segments, and the discrete Fourier transform is applied to each segment to obtain the power spectrum. The power spectrum is then filtered using a Mel filter bank, which distributes the frequencies according to the Mel scale. Finally, normalized cepstral coefficients are calculated to reduce variations caused by the global level of the audio signal[4].

## 2.4 Architecture

Architecture refers to the interconnection structure of parameterized modules. This structure can be viewed as a mathematical function with its parameters or as a computational graph consisting of nodes that represent operations and links that symbolize variables or parameters. The architecture for cough recognition includes several parameters. Deep learning involves building a multilayer network by arranging and connecting modules, and then training this architecture using gradient descent after computing the gradient by backpropagation [6]. Our deep learning architecture is based on convolutional feed-forward neural networks, which use all connected layers or ConvNet. Convolutional networks utilize the discrete convolution operation, a mathematical filtering process that involves computing a weighted sum over a window of an image or signal and sliding this window across the entire input signal while recording the results in the output signal. The weights of the sum are uniform for all windows. The output signal is translated if the input signal is translated, but otherwise remains unchanged. Convolution allows for the detection of patterns regardless of their position in the input signal. ConvNet includes 3 convolutional layers of 3x3 and max-pooling of 4x2. The last two layers consist of two fully connected layers with dimensions of 1024x1024, followed by a fully connected layer with a dimension of 1024. Dropout is applied to the fully connected layers.

Figure 7 illustrates the connection between the three layers. The convolution produces a feature map with both positive and negative values, as the weights can be negative. When passing
<figure><img src="image_6.png" /><figcaption>Figure 4: Statistics: Clinical forms of the dataset.</figcaption></figure>

fies the elementary sound present in the middle of the window. Additionally, the derivative of the log-scaled hybrid spectrogram was used to create additional channels, enhancing the comprehension and discrimination of acoustic features.

Figure 6 shows a flowchart that describes the algorithm for converting cough audio signals into MFCC feature vectors [7]. Converting an audio signal into MFCC (Mel-Frequency Cepstral Coefficients) spectrum follows this path: The audio signal is divided into fixed-length segments, and the discrete Fourier transform is applied to each segment to obtain the power spectrum. The power spectrum is then filtered using a Mel filter bank, which distributes the frequencies according to the Mel scale. Finally, normalized cepstral coefficients are calculated to reduce variations caused by the global level of the audio signal [4].

## 2.4 Architecture

Architecture refers to the interconnected structure of parameterized modules. This structure can be seen as a mathematical function with its parameters or as a computational graph consisting of nodes representing operations and links symbolizing variables or parameters. The architecture for cough recognition involves several parameters. In deep learning, a multilayer network is built by arranging and connecting modules, and then training this architecture using gradient descent after computing the gradient by backpropagation [6]. Our deep learning architecture is based on convolutional feed-forward neural networks, which use all connected layers, also known as ConvNet. Feed-forward neural networks are indispensable tools in the domain of deep learning, offering a plethora of advantages that elucidate their surging popularity. Their universality of approximation allows them to model complex relationships between inputs and outputs, making them flexible and suitable for a variety of problems. In other words, the choice of a feed-forward network depends on various factors, including the nature of the data, the sample size, and the modelling objectives. In this study, The choice of this tool is a result of the nature of the dataset, which is imbalanced. It is preferable to use a feed-forward
<figure><img src="image_8.png" /><figcaption>Figure 6: Flowchart: Converting cough audio signals into MFCC feature vectors.</figcaption></figure>

through the ReLU layer, negative values are set to zero while positive values remain unchanged. The resulting arrays are referred to as feature maps. This non-linear operation enables the system to identify patterns in the image. The ReLU layer is then followed by a pooling layer, which divides the feature map output into non-overlapping tiles or windows. Each neuron in the pooling layer takes a window of 8 numbers and calculates its maximum value, resulting in a process called max pooling. This technique is used to create a representation of the input image that is invariant to small shifts in the patterns. The max pooling operation selects the highest value from its input, which corresponds to the most prominent pattern in its receptive field. The convolutional network is composed of a series of layers consisting of convolutions, ReLU activations, and pooling.

<figure><img src="image_9.png" /><figcaption>Figure 7: Architecture.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 5: Waveform for each cough category.</figcaption></figure>

network in order to minimise the risk of overfitting. Convolutional networks use the discrete convolution operation, a mathematical filtering process that involves computing a weighted sum over a window of an image or signal and sliding this window across the entire input signal while recording the results in the output signal. The weights of the sum are uniform for all windows. The output signal is translated if the input signal is translated; otherwise, it remains unchanged. Convolution allows for the detection of patterns regardless of their position in the input signal. The ConvNet includes 3 convolutional layers of 3x3 and max-pooling of 4x2. The last two layers consist of two fully connected layers with dimensions of 1024x1024, followed by a fully connected layer with a dimension of 1024. Dropout is applied to the fully connected layers. Figure 7 illustrates the connection between the three layers. The convolution produces a feature map with both positive and negative values, as the weights can be negative. When passing through the ReLU layer, negative values are set to zero, while positive values remain unchanged. The resulting arrays are referred to as feature maps. This non-linear operation enables the system to identify patterns in the image. The ReLU layer is then followed by a pooling layer, which divides the feature map output into non-overlapping tiles or windows. Each neuron in the pooling layer takes a window of 8 numbers and calculates its maximum value, resulting in a process called max pooling. This technique is used to create an image representation that is invariant to small pattern shifts. The max-pooling operation selects the highest value from its input, which corresponds to the most prominent pattern in its receptive field. The convolutional network is composed of a series of layers consisting of convolutions, ReLU activations, and pooling.

## 2.5 Training and Build

Learning involves gradually reducing errors in the system by trying, making mistakes, and readjusting. Each parameter readjustment overwrites the previous parameter values. For a given training example, where an MFCC x feature vectors is associated with output y, the error
## 2.5 Training and Build

Learning involves gradually reducing the system's errors by trying, making mistakes, and readjusting. Each parameter readjustment overwrites the previous parameter values. For a given training example, where an MFCC x image is associated with output y, the error is measured by a simple number that indicates the distance between the output produced by the machine y = f(x,w) and the desired output y. The parameter vector is represented by w. The error for each learning example, each pair (x,y), is measured by a cost function C(x,y,w). The cost function measures the difference between the behavior of a model and the desired behavior. Learning involves adapting the system to reduce errors and minimize the cost function. To train our model, The data is first grouped based on its dependence and then labelled into four classes ranging from 1 to 4, based on the expected model outputs. The dataset is then split into training and test sets in an 80:20 ratio. The loss function employed is cross-entropy, while accuracy is measured using the accuracy score and the F1-score obtained after this calculation. The F1 score is a metric that combines a model's precision and recall scores. The Adam method is used as the learning optimizer. The Adam method is utilised for gradient backpropagation [5], which computes the gradient of a cost function. This backpropagation of gradients is the adjoint state applied to multi-layer networks, it allows to calculate the gradient of a cost function. The principle consists in propagating a signal backwards in the network, thus propagating the partial derivatives (gradients). The model is trained for 100 epochs with a batch size of 30 to adjust the weights or variables of the cost function.

# III RESULTS AND DISCUSSIONS

## 3.1 Results

The sample studied is a short sequence or excerpt from a human voice recording. These recordings are coughing sounds of patients. They are classified in four categories: Healthy, Sick without COVID-19, Sick with Severe COVID-19 and Not Severe. During data acquisition for model training, it is important to consider quiet environments. This can reduce both systematic and random errors in audio quality. The statistical measures used to estimate the numerical results of the algorithms were: Sensitivity (or recall), which measures correctly identified true positives; Specificity (or selectivity, precision), which measures correctly identified true negatives; Accuracy, which measures the total number of samples correctly classified; F1 score, which is the harmonic mean of precision and recall.

$$F1 = \frac{2 * \text{Sensitivity} * \text{Specificity}}{\text{Sensitivity} + \text{Specificity}} \qquad (1)$$

The architecture of the convolutional neural network provides a cross-validation accuracy of 90.33% and an F1-score of 89%. The feature extraction process: The conversion of cough sounds into MFCC spectrograms demonstrates a strong correlation between the cepstral characteristics of images within the same class, due to the similarity of the cepstral vectors. However, for two MFCC images from different classes, the distance between the cepstral vectors is significant.

Figure 8 displays four MFCC spectrums, with each spectrum representing a sample of data from a different class. The spectrum of severe COVID-19 is brighter than the others, indicating that it has more energy. The four spectrums are visibly distinct, and this fundamental difference produced by the MFCC method makes classification by CNN easy.
<figure><img src="image_8.png" /><figcaption>Figure 6: Flowchart: Converting cough audio signals into MFCC feature vectors.</figcaption></figure>

is measured by a simple number indicating the distance between the output produced by the machine ($y = f(x,w)$) and the desired output $y$. The parameter vector is represented by $w$. The error for each learning example, each pair $(x,y)$, is measured by a cost function $C(x,y,w)$. The cost function measures the difference between the behavior of a model and the desired behavior. Learning involves adapting the system to reduce errors and minimize the cost function.

To train our model, the data is first grouped based on its dependence and then labeled into four classes ranging from 1 to 4, based on the expected model outputs. The dataset is then split into training and test sets in an 80:20 ratio. The loss function employed is cross-entropy, while accuracy is measured using the accuracy score and the F1-score obtained after this calculation. The F1 score is a metric that combines a model's precision and recall scores.

The Adam method is used as the learning optimizer. It is used for gradient backpropagation [5], which computes the gradient of a cost function. This backpropagation of gradients is the adjoint state applied to multi-layer networks, allowing the calculation of the gradient of a cost function. The principle consists of propagating a signal backward in the network, thus propagating the partial derivatives (gradients). The model is trained for 100 epochs with a batch size of 30 to adjust the weights or variables of the cost function. The algorithmic model is developed by our laboratory using the Python programming language. The complete experimental dataset, including all relevant source code, is stored in our internal repository, thus facilitating reproduction of the reported results.

# III RESULTS AND DISCUSSIONS

## 3.1 Results

The sample studied is a short sequence or excerpt from a human voice recording. These recordings are coughing sounds of patients and are classified into four categories: Healthy, Sick without COVID-19, Sick with Severe COVID-19, and Not Severe. It is important to consider
Figure 9 displays the outcome of the model compilation. This learning curve illustrates the progression of loss and validation loss values throughout the iterations or 100 training epochs of the convolutional neural network model. It is used to analyse the model's performance and adjust hyperparameters to achieve better results.

<figure><img src="image_10.png" /><figcaption>Figure 8: Results of Feature Extraction.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Figure 9: Graph derived from the Model Compilation.</figcaption></figure>

## 3.2 Discussions

Our study presents three main results. The first main result is the classification of four categories of cough noises: cough of sick patients without COVID-19, cough of healthy patients and cough of sick patients with severe and not severe COVID-19. The second result concerns the learning by Adams method and the third result is the extraction of the characteristics of each
<figure><img src="image_9.png" /><figcaption>Figure 7: Architecture for the classification of cough sounds in the context of COVID-19, a variant of ConvNet designed by our laboratory.</figcaption></figure>

quiet environments during data acquisition for model training to reduce systematic and random errors in audio quality. Statistical measures used to estimate the numerical results of the algorithms include Sensitivity (or recall), which measures correctly identified true positives; Specificity (or selectivity, precision), which measures correctly identified true negatives; Accuracy, which measures the total number of samples correctly classified; and F1 score, which is the harmonic mean of precision and recall.

$$Sensitivity = \frac{TP}{TP + FN} \qquad (1)$$

$$Specificity = \frac{TN}{TN + FP} \qquad (2)$$

$$Accuracy = \frac{TP + TN}{TP + TN + FP + FN} \qquad (3)$$

Where: TP (true positive) = correctly identified; TN (true negative) = correctly rejected; FN (false negative) = incorrectly rejected; FP (false positive) = incorrectly identified [24].

$$F1 = \frac{2 * Sensitivity * Specificity}{Sensitivity + Specificity} \qquad (4)$$

The convolutional neural network architecture provides an F1-score of 89%, an accuracy of 90.33% and sensitivity of 87.3%. The feature extraction process involves converting cough sounds into MFCC spectrograms, demonstrating a strong correlation between the cepstral characteristics of images within the same class, and significant differences for images from different classes.

Figure 8 displays four MFCC spectrums, each representing a sample from a different class. The spectrum of severe COVID-19 is brighter, indicating higher energy and the four spectrums are visibly distinct, making classification by CNN easy.

Figure 9 shows the outcome of the model compilation, presenting the learning curve illustrating the progression of loss and validation loss values throughout 100 training epochs of the convolutional neural network model. This curve is used to analyze the model's performance and adjust hyperparameters for better results. The algorithmic model was developed by our laboratory using the Python programming language. The complete experimental dataset, including all relevant source code, is stored in our internal repository, thus facilitating reproduction of the reported results.
<table><caption>Table 1: Positioning the Work in the Field.</caption><thead><tr><th>references</th><th>methods</th><th>metrics</th></tr></thead><tbody><tr><td>Yuh-Shyan<br>Chen et al[26]</td><td>Method of MFCCs<br>The Cube-Transformer model</td><td>Accuracy<br>by 1.5%</td></tr><tr><td>Ali Imran<br>et al[15]</td><td>Method of MFCCs<br>and Multi-Class classifier</td><td>F1-Score<br>89%</td></tr><tr><td>Moschovis<br>et al [10]</td><td>ResAppDx<br>algorithm</td><td>76.5%<br>sensitivity</td></tr><tr><td>BROWN<br>et al[11]</td><td>binary machine<br>learning classifier</td><td>AUC of<br>above 80%</td></tr><tr><td>This Work</td><td>method of MFCCs<br>ConvNet</td><td>90.33% accuracy<br>and 89% F1-score</td></tr></tbody></table>

cough category.

This study provides a more thorough analysis of the diagnosis of COVID-19 by classifying it into four categories. The severity of COVID-19 is a particular focus of this study. The study by Ali Imran et al. focuses on the diagnosis of bronchitis, pertussis, COVID-19, and Normal using AI. Although highly original, the study by Ali Imran et al does not mention the severity of COVID-19, which is alluded to in the clinical information on disease states. Diagnosing the severity of COVID-19 enables appropriate follow-up and monitoring of the patient to prevent complications. This initial diagnosis will facilitate effective management of hospital resources by directing them towards the most severely affected patients.

It is remarkable to mention that our CNN model with 90.33% accuracy surpasses the bmodels of R. Islam whose CNN model for cough detection had an accuracy of 88.5% and this model also surpasses the work of C. Brown et al. on COVID-19 detection with an accuracy of 80% [24, 25]. The reasons for selecting CNNs for MFCC image classification are as follows: CNN architecture is specialised in image classification due to the mathematical convolution and pooling operator. In contrast, RNNs are designed more for signals, so the inputs are 1D. CNNs can easily capture features in images and facilitate convolution parallelism, which makes processing fast. The CNN achieves transactional invariance in images by sharing weights between layers, while RNNs are sensitive to the position of data in signal sequences.

The cepstral characteristics of MFCC images make CNNs suitable for classification. This technique enables correlation of images in each class category. In the work of Yuh-Shyan Chen et al, Ali Imran et al, the MFCC method was also used as an extractor. Table 1 shows the positioning of the paper in relation to other papers in the field.

## IV CONCLUSION

This research described the realization of an intelligent tool for the preliminary diagnosis of respiratory diseases based on cough sounds, such as COVID-19. Aid diagnosis approach based on deep learning allows to detect cough sounds of sick people without COVID-19, with severe and mild COVID-19, and also to detect cough sounds of healthy patients. To achieve this, a CNN model was built, trained on data collected by our team, also by other research teams. The experimental result, after validation of the model on data, shows that this CNN
<figure><img src="image_10.png" /><figcaption>Figure 8: Results of feature extraction: The Mel Frequency Cepstral Coefficients (MFCCs) to explore the fundamental characteristics of coughing sounds associated with COVID-19.</figcaption></figure>

## 3.2 Discussions

Our study reveals three key findings. The initial finding pertains to the categorization of four types of cough sounds.: cough of sick patients without COVID-19, cough of healthy patients, and cough of sick patients with severe and non-severe COVID-19 The second result relates to the learning process using the Adams method, and the third result focuses on extracting characteristics for each cough category.

<figure><img src="image_11.png" /><figcaption>Figure 9: The graph, derived from the model Compilation, presents the optimisation of ConvNet model parameters by gradient backpropagation.</figcaption></figure>
model detects with great skill the four classes. Given that the experimental report gives a good result according to the metrics used. Given the benefit of this proposed model, this work makes an important scientific contribution. Firstly, it allows a quick, vocal diagnosis of respiratory diseases. Secondly, the model distinguishes between mild and severe COVID-19 for an efficient management of patients in hospitals.

V ACKNOWLEDGEMENTS

Our prospective virtue comes from the Lord Jesus Christ. We thank the patients for their agreement to the audio recordings and also the research laboratory COUGHVID for their contribution in data.

REFERENCES

Publications

[1] J. Chan, C. Ng, Y. Chan, T. Mok, S. Lee, S. Chu, W. Law, M. Lee, and P. Li. "Short term outcome and risk factors for adverse clinical outcomes in adults with severe acute respiratory syndrome (SARS)". In: *Thorax* 58.8 (2003), pages 686–689.

[2] A. Spanias, T. Painter, and V. Atti. *Audio signal processing and coding*. John Wiley & Sons, 2006.

[3] A. Morice, G. Fontana, M. Belvisi, S. Birring, K. Chung, P. V. Dicpinigaitis, J. Kastelik, L. McGarvey, J. Smith, M. Tatar, et al. "ERS guidelines on the assessment of cough". In: *European respiratory journal* 29.6 (2007), pages 1256–1276.

[4] L. Muda, M. Begam, and I. Elamvazuthi. "Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques". In: *arXiv preprint arXiv:1003.4083* (2010).

[5] C. Farabet, C. Couprie, L. Najman, and Y. LeCun. "Learning hierarchical features for scene labeling". In: *IEEE transactions on pattern analysis and machine intelligence* 35.8 (2012), pages 1915–1929.

[6] Y. LeCun, Y. Bengio, and G. Hinton. "Deep learning". In: *nature* 521.7553 (2015), pages 436–444.

[7] T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun. "Very deep multilingual convolutional neural networks for LVCSR". In: *2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)*. IEEE. 2016, pages 4955–4959.

[8] K. Choi, G. Fazekas, M. Sandler, and K. Cho. "A comparison of audio signal preprocessing methods for deep neural networks on music tagging". In: *2018 26th European Signal Processing Conference (EUSIPCO)*. IEEE. 2018, pages 1870–1874.

[9] T. Iqbal, Q. Kong, M. D. Plumbley, and W. Wang. "General-purpose audio tagging from noisy labels using convolutional neural networks". In: *Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)*. Tampere University of Technology. 2018, pages 212–216.

[10] P. Moschovis, E. Sampayo, P. Porter, U. Abeyratne, G. Doros, V. Swarnkar, R. Sharan, and J. Carl. "A cough analysis smartphone application for diagnosis of acute respiratory illnesses in children". In: *A27. Pediatric Lung Infection and Critical Care Around the World*. American Thoracic Society, 2019, A1181–A1181.
<table><caption>Table 1: Positioning the Work in the Field.</caption><thead><tr><th>References</th><th>Methods</th><th>Dataset size</th><th>Metrics</th></tr></thead><tbody><tr><td>Schuller et al. [21]</td><td>CNN</td><td>1427 cough sound</td><td>80.7% Accuracy</td></tr><tr><td>Ali Imran et al. [16]</td><td>Method of MFCCs and CNN</td><td>317 cough sound</td><td>92.6% Accuracy</td></tr><tr><td>Bansal et al. [11]</td><td>CNN</td><td>871 cough sound</td><td>70.5% Accuracy</td></tr><tr><td>Mohamed and Seyedali [28]</td><td>binary machine DL transfer model</td><td>1457 cough sound</td><td>94.9% Accuracy</td></tr><tr><td>This Work</td><td>method of MFCCs ConvNet</td><td>3300 cough sound</td><td>90.33% accuracy</td></tr></tbody></table>

This study presents a thorough analysis of COVID-19 diagnosis by classifying it into four groups, focusing specifically on the severity of the illness. Ali Imran et al.'s study focuses on diagnosing bronchitis, pertussis, COVID-19, and Normal using AI [16]. However, the study does not address the severity of COVID-19, although it is alluded to in the clinical information on disease states. Accurate diagnosis of the severity of COVID-19 allows for appropriate follow-up and monitoring of patients to prevent complications. This initial diagnosis will aid in the effective management of hospital resources by directing them toward the most severely affected patients.

It's worth noting that our CNN model, with a 90.33% accuracy rate, outperforms the models of Schuller et al., whose CNN model for cough detection had an accuracy of 80.7%, and also surpasses the work of C. Bansal et al. on COVID-19 detection with an accuracy of 70.5% [11, 21].

The reasons for selecting CNNs for MFCC feature vectors classification are as follows: In this study, convolutional neural networks (CNNs) are employed to accommodate data imbalance. This is performed through the use of techniques such as class weighting and oversampling of minority classes, which enable CNNs to effectively learn from imbalanced dataset. CNN architecture specializes in image classification due to the mathematical convolution and pooling operator. In contrast, RNNs are designed more for signals, so the inputs are 1D. CNNs can easily capture features in images and enable convolution parallelism, which increases processing speed. CNN achieves translational invariance in images by sharing weights between layers, while RNNs are sensitive to the position of data in signal sequences. Deep transfer models such as ResNet, DenseNet, LeNet-5, AlexNet, GoogleNet, VGG 16 and NasNetmobile are extremely powerful and can be a quick solution to many automatic classification tasks, but building this ConvNet model offers significant advantages in terms of the specificity of this particular four-class classification task and control over the learning process. By training this model from scratch, we can better control the learning process and adapt the network architecture to this specific classification task. This leads to fine tuning of the network.

The cepstral characteristics of MFCC feature vectors make CNNs suitable for classification. This technique enables the correlation of images in each class category. In the work of Ali Imran et al., the MFCC method was also used as an extractor. Table 1 shows the positioning of the paper to other papers in the field.
[11] C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, and C. Mascolo. "Exploring Automatic Diagnosis of COVID-19 from Crowd-sourced Respiratory Sound Data (preprint)". In: (2020).

[12] R. Chowdhury, K. Heng, M. S. R. Shawon, G. Goh, D. Okonofua, C. Ochoa-Rosales, V. Gonzalez-Jaramillo, A. Bhuiya, D. Reidpath, S. Prathapan, et al. "Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries". In: *European journal of epidemiology* 35 (2020), pages 389–399.

[13] S. E. Eikenberry, M. Mancuso, E. Iboi, T. Phan, K. Eikenberry, Y. Kuang, E. Kostelich, and A. B. Gumel. "To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic". In: *Infectious disease modelling* 5 (2020), pages 293–308.

[14] A. E. Gorbalenya, S. C. Baker, R. S. Baric, R. J. de Groot, C. Drosten, A. A. Gulyaeva, B. L. Haagmans, C. Lauber, A. M. Leontovich, B. W. Neuman, et al. "Severe acute respiratory syndrome-related coronavirus: The species and its viruses–a statement of the Coronavirus Study Group". In: *BioRxiv* (2020).

[15] A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, M. S. Riaz, K. Ali, C. N. John, M. I. Hussain, and M. Nabeel. "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app". In: *Informatics in medicine unlocked* 20 (2020), page 100378.

[16] D. Jakhar and I. Kaur. "Artificial intelligence, machine learning and deep learning: definitions and differences". In: *Clinical and experimental dermatology* 45.1 (2020), pages 131–132.

[17] G. Kampf, D. Todt, S. Pfaender, and E. Steinmann. "Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents". In: *Journal of hospital infection* 104.3 (2020), pages 246–251.

[18] C. H. Nkwayep, S. Bowong, J. Tewa, and J. Kurths. "Short-term forecasts of the COVID-19 pandemic: a study case of Cameroon". In: *Chaos, Solitons & Fractals* 140 (2020), page 110106.

[19] R. Ranjan. "Predictions for COVID-19 outbreak in India using epidemiological models". In: *MedRxiv* (2020), pages 2020–04.

[20] R. Verity, L. C. Okell, I. Dorigatti, P. Winskill, C. Whittaker, N. Imai, G. Cuomo-Dannenburg, H. Thompson, P. G. Walker, H. Fu, et al. "Estimates of the severity of coronavirus disease 2019: a model-based analysis". In: *The Lancet infectious diseases* 20.6 (2020), pages 669–677.

[21] R. Wathore, A. Gupta, H. Bherwani, and N. Labhasetwar. "Understanding air and water borne transmission and survival of coronavirus: Insights and way forward for SARS-CoV-2". In: *Science of the total environment* 749 (2020), page 141486.

[22] H. Fenaux, T. Ghelfenstein-Ferreira, M. Salmona, N. Mahjoub, L. Feghoul, S. Maylin, M.-L. Chaix, M. Minier, A. Gabassi, J. Le Goff, et al. "Interpretation of single target positivity among SARS-CoV-2 RT-PCR result tests". In: *Journal of Clinical Virology Plus* 1.1-2 (2021), page 100021.

[23] J. L. Gevertz, J. M. Greene, C. H. Sanchez-Tapia, and E. D. Sontag. "A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing". In: *Journal of Theoretical Biology* 510 (2021), page 110539.

[24] M. R. Kamble, J. A. Gonzalez-Lopez, T. Grau, J. M. Espin, L. Cascioli, Y. Huang, A. Gomez-Alanis, J. Patino, R. Font, A. M. Peinado, et al. "PANACEA cough sound-
# IV CONCLUSION

This study discusses the development of an intelligent tool for early diagnosis of respiratory diseases, specifically using cough sounds to detect conditions such as COVID-19. The diagnostic approach, employing deep learning, can identify cough sounds from individuals with no COVID-19, as well as those with mild and severe cases, and healthy individuals. A CNN model was created and trained on data collected by our team and other research groups. The experimental results, following validation of the model on the data, demonstrate the CNN model's robust ability to detect all four categories. Based on the favorable results achieved with the metrics employed, this proposed model demonstrates considerable scientific merit. It enables rapid vocal diagnosis of respiratory problems and effectively differentiates between mild and severe COVID-19 for improved patient management in healthcare settings.

In order to facilitate the provision of real-time diagnostics by our system, we are currently developing a pipeline comprising this model and an adaptive filter. The filter in question analyses the input signal in real time, adjusting its parameters in accordance with the background noise present in order to minimise its impact on the recording. Furthermore, the performance of the model is to be enhanced through the investigation of additional data standardisation techniques.

# V ACKNOWLEDGEMENTS

We thank the patients for their agreement to the audio recordings and also the research laboratory COUGHVID for their contribution in data.

# REFERENCES

## Publications

[1] J. Chan, C. Ng, Y. Chan, T. Mok, S. Lee, S. Chu, W. Law, M. Lee, and P. Li. "Short term outcome and risk factors for adverse clinical outcomes in adults with severe acute respiratory syndrome (SARS)". In: *Thorax* 58.8 (2003), pages 686–689.

[2] A. Spanias, T. Painter, and V. Atti. *Audio signal processing and coding*. John Wiley & Sons, 2006.

[3] A. Morice, G. Fontana, M. Belvisi, S. Birring, K. Chung, P. V. Dicpinigaitis, J. Kastelik, L. McGarvey, J. Smith, M. Tatar, et al. "ERS guidelines on the assessment of cough". In: *European respiratory journal* 29.6 (2007), pages 1256–1276.

[4] L. Muda, M. Begam, and I. Elamvazuthi. "Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques". In: *arXiv preprint arXiv:1003.4083* (2010).

[5] C. Farabet, C. Couprie, L. Najman, and Y. LeCun. "Learning hierarchical features for scene labeling". In: *IEEE transactions on pattern analysis and machine intelligence* 35.8 (2012), pages 1915–1929.

[6] Y. LeCun, Y. Bengio, and G. Hinton. "Deep learning". In: *nature* 521.7553 (2015), pages 436–444.

[7] T. Sercu, C. Puhrsch, B. Kingsbury, and Y. LeCun. "Very deep multilingual convolutional neural networks for LVCSR". In: *2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)*. IEEE. 2016, pages 4955–4959.

[8] K. Choi, G. Fazekas, M. Sandler, and K. Cho. "A comparison of audio signal preprocessing methods for deep neural networks on music tagging". In: *2018 26th European Signal Processing Conference (EUSIPCO)*. IEEE. 2018, pages 1870–1874.
based diagnosis of COVID-19 for the DiCOVA 2021 Challenge". In: *arXiv preprint arXiv:2106.04423* (2021).

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[26] Y.-S. Chen, C.-S. Hsu, and B.-X. Yang. "The Design of a Cough Disease Classification System Using Cube-Transformer". In: *2023 International Conference on Smart Applications, Communications and Networking (SmartNets)*. IEEE. 2023, pages 1-6.

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[30] *Social Stigma associated with COVID-19*. https://www.unicef.org/documents/social-stigma-associated-coronavirus-disease-covid-19/. Updated: 2023-03-24.
[9] T. Iqbal, Q. Kong, M. D. Plumbley, and W. Wang. "General-purpose audio tagging from noisy labels using convolutional neural networks". In: *Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)*. Tampere University of Technology. 2018, pages 212–216.

[10] P. Moschovis, E. Sampayo, P. Porter, U. Abeyratne, G. Doros, V. Swarnkar, R. Sharan, and J. Carl. "A cough analysis smartphone application for diagnosis of acute respiratory illnesses in children". In: *A27. Pediatric Lung Infection and Critical Care Around the World*. American Thoracic Society, 2019, A1181–A1181.

[11] V. Bansal, G. Pahwa, and N. Kannan. "Cough Classification for COVID-19 based on audio mfcc features using Convolutional Neural Networks". In: *2020 IEEE international conference on computing, power and communication technologies (GUCON)*. IEEE. 2020, pages 604–608.

[12] C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, and C. Mascolo. "Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data (preprint)". In: (2020).

[13] R. Chowdhury, K. Heng, M. S. R. Shawon, G. Goh, D. Okonofua, C. Ochoa-Rosales, V. Gonzalez-Jaramillo, A. Bhuiya, D. Reidpath, S. Prathapan, et al. "Dynamic interventions to control COVID-19 pandemic: a multivariate prediction modelling study comparing 16 worldwide countries". In: *European journal of epidemiology* 35 (2020), pages 389–399.

[14] S. E. Eikenberry, M. Mancuso, E. Iboi, T. Phan, K. Eikenberry, Y. Kuang, E. Kostelich, and A. B. Gumel. "To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic". In: *Infectious disease modelling* 5 (2020), pages 293–308.

[15] A. E. Gorbalenya, S. C. Baker, R. S. Baric, R. J. de Groot, C. Drosten, A. A. Gulyaeva, B. L. Haagmans, C. Lauber, A. M. Leontovich, B. W. Neuman, et al. "Severe acute respiratory syndrome-related coronavirus: The species and its viruses–a statement of the Coronavirus Study Group". In: *BioRxiv* (2020).

[16] A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, M. S. Riaz, K. Ali, C. N. John, M. I. Hussain, and M. Nabeel. "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app". In: *Informatics in medicine unlocked* 20 (2020), page 100378.

[17] D. Jakhar and I. Kaur. "Artificial intelligence, machine learning and deep learning: definitions and differences". In: *Clinical and experimental dermatology* 45.1 (2020), pages 131–132.

[18] G. Kampf, D. Todt, S. Pfaender, and E. Steinmann. "Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents". In: *Journal of hospital infection* 104.3 (2020), pages 246–251.

[19] C. H. Nkwayep, S. Bowong, J. Tewa, and J. Kurths. "Short-term forecasts of the COVID-19 pandemic: a study case of Cameroon". In: *Chaos, Solitons & Fractals* 140 (2020), page 110106.

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