Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network

Abstract

In daily life, there are a variety of complex sound sources. It is important to effectively detect certain sounds in some situations. With the outbreak of COVID-19, it is necessary to distinguish the sound of coughing, to estimate suspected patients in the population. In this paper, we propose a method for cough recognition based on a Mel-spectrogram and a Convolutional Neural Network called the Cough Recognition Network (CRN), which can effectively distinguish cough sounds.

Full text

Cough Recognition Based on
Mel-Spectrogram and Convolutional
Neural Network
Quan Zhou
1
, Jianhua Shan
1
, Wenlong Ding
1
, Chengyin Wang
1
, Shi Yuan
1
, Fuchun Sun
2
,
Haiyuan Li
3
and Bin Fang
2
*
1
Anhui Province Key Laboratory of Special Heavy Load Robot, Anhui University of Technology, Ma’anshan, China,
2
Beijing
National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua
University, Beijing, China,
3
Robotics Institute, School of Automation, Beijing University of Posts and Telecommunications, Beijing,
China
In daily life, there are a variety of complex sound sources. It is important to effectively detect
certain sounds in some situations. With the outbreak of COVID-19, it is necessary to
distinguish the sound of coughing, to estimate suspected patients in the population. In this
paper, we propose a method for cough recognition based on a Mel-spectrogram and a
Convolutional Neural Network called the Cough Recognition Network (CRN), which can
effectively distinguish cough sounds.
Keywords: cough recognition, mel-spectrogram, CNN, deep learning, audio, COVID-19
INTRODUCTION
As a disease with a long incubation period and high infection rate, COVID-19 has caused millions of
people to be infected and hundreds of thousands of people to died. How to avoid the rapid spread of
the epidemic and effectively control the number of infected people has become an urgent issue. Asif
et al. found that data from 10,172 COVID-19 laboratory-confirmed cases have shown a correlation
with coughing in 54.08% (Sattar Hashmi and Asif, 2020). Therefore, coughing, as a typical symptom
of pneumonia, is of great significance in controlling the potential infectious source if it can be quickly
and accurately monitored in the population.
Many scholars have studied how to extract features of sound and recognize the sound. Mel
Frequency Cepstrum Coefficient (MFCC), as a method of extracting audio features (Shintri and
Bhatia, 2015), is widely used in various audio recognition tasks. Xie et al. used MFCC to recognize
abnormal voice (Xie et al., 2012). Wang et al. proposed to recognize speech emotion based on
improved MFCC (Wang and Hu, 2018). Suksri described a method that used MFCC extracted from
the speech signals of spoken words for speech recognition (Ittichaichareon et al., 2012). The Fourier
transform (FT) is also widely used in audio processing. Jozef et al. presented a new procedure for the
frequency analysis of audio signals (Pucik et al., 2014).
Although these traditional methods are very effective for the extraction of audio features,
considering the complexity of the real scene, the method of deep learning may achieve better
results. With the development of deep learning, the neural network has played an important role in
audio recognition. Oren et al. proposed spectral representations for convolutional neural networks
(Rippel et al., 2015). Some LSTM-based networks for speech recognition are also presented (Pundak
and Sainath, 2017;Trianto et al., 2018). Compared with traditional methods, deep learning can
extract more complex and robust features.
For cough recognition, various methods are proposed. Cough signals are usually obtained by
audio or inertial sensors, which can detect the vibration caused by coughing. These sensors include a
Edited by:
Shalabh Gupta,
University of Connecticut,
United States
Reviewed by:
Jing Yang,
University of Connecticut,
United States
Shugong Xu,
Shanghai University, China
*Correspondence:
Bin Fang
fangbin@mail.tsinghua.edu.cn
Specialty section:
This article was submitted to
Smart Sensor Networks
and Autonomy,
a section of the journal
Frontiers in Robotics and AI
Received: 04 July 2020
Accepted: 09 April 2021
Published: 07 May 2021
Citation:
Zhou Q, Shan J, Ding W, Wang C,
Yuan S, Sun F, Li H and Fang B (2021)
Cough Recognition Based on Mel-
Spectrogram and Convolutional
Neural Network.
Front. Robot. AI 8:580080.
doi: 10.3389/frobt.2021.580080
Frontiers in Robotics and AI | www.frontiersin.org May 2021 | Volume 8 | Article 5800801
ORIGINAL RESEARCH
published: 07 May 2021
doi: 10.3389/frobt.2021.580080

microphone that can be worn or placed near the user, or a
piezoelectric transducer and a high-sensitivity accelerator that
can be placed in the throat or chest area (Drugman et al., 2013;
Amoh and Odame, 2016;Elfaramawy et al., 2018).
Infante et al. used a machine learning method to recognize
dry/wet cough (Infante et al., 2017). Semi-supervized Tree
Support Vector Machine is proposed for cough recognition
and detection (Hoa et al., 2011). K-NN is also an efficient tool
that is often used for cough recognition (Hoyos Barcelo et al.,
2017;Vhaduri et al., 2019).
In addition, the Artificial Neural Network (ANN), Gaussian
Mixture Model (GMM), Support Vector Machine (SVM), and
other methods are also used for cough recognition (Drugman
et al., 2011).
The difficulty of cough recognition mainly lies in the
distinction of background noise. There are many kinds of
sound mixed together in daily scenes. How to effectively
distinguish between coughing and other sounds has become a
difficult problem to be solved.
In this paper, we propose a cough recognition method based
on a Mel-spectrogram and a Convolutional Neural Network
(CNN). First, we enhance the audio data and mix the voice in
various complex scenes. Then, we preprocess the data to ensure
the consistency of data length and convert it into a Mel-
spectrogram. At last, we build a CNN-based model to classify
the cough using the Mel-spectrogram. At the same time, we make
comparisons with some other common methods. After the
experiment result comparison, it can be seen that this method
can effectively identify and detect coughing in complex scenes. It
can be seen that the cough recognition model based on a Mel-
spectrogram and a CNN can achieve good results.
MATERIALS AND METHODOLOGY
As shown in Figure 1, the work-flow of our cough classification
model is presented.
Data Augmentation
Considering the natural environment, sound is not produced by a
single sound source and the received sound is often the mix of
multiple sounds. In order to improve the recognition effect and
robustness, we enhance the data, using noise and human voice to
mix the cough data.
We selected several audio datasets to make data augmentation,
such as the ESC-50 dataset (Piczak, 2015) and the Speech
Commands Data Set (Warden, 2018). All cough data comes
from the ESC-50 dataset.
Positive Samples 1: Cough. After audio segmentation, we
select all cough audio samples as positive samples. We also
obtain more cough audio samples by increasing and
decreasing the volume.
Positive Samples 2 and 3: Cough + Human Sound and
Cough + Natural Sound. In order to enhance the robustness of the
model, we also mix cough audio with natural sound (wind, rain,
door-clock, footsteps, and other common noises) and human sound
(mainly including commonly spoken words such as “go,”“up,”
“right,”and so on) respectively as positive samples 2 and 3.
In all the mixed audio, the volume of the coughing sound is
adjusted to produce more mixed outcomes of different cough
sounds and other sounds.
All of the original and processed cough audio data are labeled
as “cough.”
Negative Samples 1: Human Sound. We choose human sounds
(mainly include “go,”“up”and, some other common human
noises, and all sounds come from different samples which are
unused for cough augmentation) from the datasets above as one
of the negative samples. So our model can distinguish between
cough sounds and human sounds. And all human sounds were
mixed with white noise, pink noise, and so on.
Negative Samples 2: Natural Sound: We choose natural noise
(wind, rain, pouring-water, footsteps, and other common sounds.
All sounds come from different samples which are unused for
cough augmentation) from the datasets above as other negative
samples.
All human sound and natural sound data are labeled as
“others.”
In the end, we have cough sounds, mixed cough audio with
natural noise, and mixed cough audio with human sounds as
positive samples. At the same time, human sounds and natural
sounds are taken as negative samples.
Data Preprocess
Considering that audio with a too short length of time may make
it difficult to recognize the sound, and that audio with a too long
length of time may cause the superposition of a variety of
uncorrelated sounds, we choose the length of 1 s as the input.
And the duration of cough samples in the original dataset is
different, so we select the audio containing coughing and divide it
into seconds.
FIGURE 1 | The work-flow diagram.
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Zhou et al. Cough Recognition

Mel-Spectrogram
The Mel spectrum contains a short-time Fourier transform
(STFT) for each frame of the spectrum (energy/amplitude
spectrum), from the linear frequency scale to the logarithmic
Mel-scale, and then goes through the filter bank to get the
eigenvector, these eigenvalues can be roughly expressed as the
distribution of signal energy on the Mel-scale frequency.
After the audio data are processed into 1 s-long data, we
transform all the data into Mel-spectrograms so that we can
train the convolutional neural networks for recognition.
Audio data usually have complex features, so it is necessary to
extract useful features to recognize the audio. The Mel-
spectrogram is one of the efficient methods for audio
processing and 8 kHz sampling is used for each audio sample.
In the experiment, we employ the Python package called
librosa for data processing and all parameters are as follows:
(nfft 1024,hop length 512,n mels 128). Then we call
the power_to_db function to convert the power spectrum
(amplitude square) to decibel (DB) units.
In Figure 2, we show some examples of Mel-spectrograms. As
can be seen from the figure, there are some differences in different
types of voices. But after mixing noise, some details will be
covered, which is helpful for us to test the cough recognition
effect of the model for the real scene. And we extract the features
of the audio and transform them into feature images, so there are
three channels like traditional color images.
Normalization
For image input, we normalize them to make the model converge
faster. For the Mel-spectrogram, we calculate the mean and
standard deviation of the three channels respectively and then
normalize them. The normalization formula is as follows:
xnorm x−mean(x)
std(x),(1)
where xdenotes the values in different channels and xnorm
denotes normalized values.
Loss Function
The recognition loss function of the model Lrec represents the
cross-entropy loss:
Lrec −
1
nyln
y+1−yln1−
y,(2)
where 
yis the model output, yis the true label, and nis the
number of samples.
Convolutional Neural Network
With the development of deep learning, more and more
deep learning methods are applied to various scenarios,
such as image recognition, image classification, speech
recognition, machine translation, etc. As a kind of deep
learning method, Convolutional Neural Networks (CNN)
are widely used in the field of computer vision. In this
section, we introduce the components of the proposed
CNN-based network.
The convolutional layer is the key of a CNN model, it can
effectively reduce the parameters of the model and make it
possible for the model to optimize. The calculation formula
for the convolutional layer is as follows:
xn
jf⎛
⎝
i∈Mj
xn−1
i*kn
ij +bn
j⎞
⎠,(3)
where xn
jis the output feature map, xn−1
iis the input feature map,
Mjis the selected area in the n−1 layer, kn
ij is weight parameter, bn
j
is bias, and fis the activation function.
After each convolutional layer, we conduct batch
normalization to make the outputs of the convolutional layer
stay identically distributed, which can improve the performance
of the model. The batch normalization formula is as follows:
yicxi−u

σ2+ε
√+β,(4)
where xiis the output of convolutional layer without activation, u
is the mean of x,σ2is the variance of x, and γand βare parameters
to learn.
After feature extraction of the convolution layer, although
the number of connections between layers has been
significantly reduced, the number of neurons in the feature
map group has not been significantly reduced. Therefore, like
other common models, we add maximum pooling layers to
solve this problem.
In the end, we use the fully connected layer as the output
layer of the model. The calculation for the fully connected
layer is:
yjf⎛
⎝
N
i1
xi*wij +bj⎞
⎠,(5)
where xis the input layer, Nis the number of input layer nodes,
wij is the weight between the links xiand yj,bjis the bias, and fis
the activation function.
FIGURE 2 | Mel-spectrograms of different voices.
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Zhou et al. Cough Recognition

Experiment Approach
The CRN was trained by an Adam optimizer, whose learning rate
is 0.0001. The max epoch and batch size were 20 and 64,
respectively. The CRN was implemented by Pytorch and
trained and tested on a computer with an Intel Core i7-
8750H, two 8 GB memory chips (DDR4), and a GPU (Nvidia
Geforce GTX 1060 6G).
Dataset Description
Before training, we need to preprocess the audio data. As mentioned
in the second part, we obtained 34,320 cough samples augmented by
different audio data, including 17,160 cough + human sound
samples, 17,160 cough + natural sound samples, 17,050 human
sounds, and 17,919 different noises. As shown in Figure 3,data
components have been provided. In order to evaluate the model
better, we use two ways to divide the processed dataset.
Random Division Dataset
After all data are processed, 80% are randomly selected as the
training set, 10% as the verification set, and 10% as the test set.
Considering that due to data augmentation, some data may leak
the features of coughing.
No-Leakage Division Dataset
After all data are processed, we select almost 80% which we
augment as the training set and 10% is augmented from
completely different cough audio as the test set. In this way,
the cough sounds of the training and test sets come from different
original data, so that we can evaluate the generalization ability of
the model.
After all data are split, the mean and variance of each channel
are calculated. They are normalized to make the model converge
better.
Performance Measurements
In order to better evaluate the performance of the model, we list
several indicators used to evaluate the model.
Accuracy
The indicator that the samples with a correct reaction
classification account for the total samples.
Recall
The ratio of the number of samples recognized correctly to the
total number of samples recognized.
Precision
The ratio of the number of samples recognized correctly to the
number of samples that should be recognized.
F1 Score
It is an index used to measure the accuracy of the binary
classification model.
Accuracy TP +TN
TP +TN +FP +FN,(6)
Recall TP
TP +FN,(7)
Precision TP
TP +FP,(8)
F1 Score 2pPrecisionpRecall
Recall +Precion ,(9)
where TP (True Positive) denotes samples of coughing that are
correctly recognized by the model. FP (False Positive) which
denotes samples of coughing that are recognized as others by the
model. TN (True Negative) which denotes samples of others that
are correctly recognized by the model. FN (False Negative) which
denotes samples of others that are incorrectly recognized as
coughing by the model.
Experiment Based on Mel-Spectrogram
+CNN
The Mel-spectrogram is an effective tool to extract hidden
features from audio and visualize them as an image. A CNN
model can effectively extract features from images, and then
completetaskssuchasclassification and recognition.
Therefore, we use the CNN model to effectively classify the
audio and to realize the accurate recognition and detection of
coughing. In Figure 4, the architecture of this model has been
illustrated.
Considering the different positions of coughing in audio, the
relative positions of coughing are also different. Before we feed
the image into the network, we first unify the image size into
256 ×256, and then randomly select 224 ×224 size parts for the
recognition of different cough positions.
RESULTS
After two methods of dataset division and training, we get the
performance of the cough recognition task.
Experiment on Random Division Dataset
As shown in Table 1,wecanfind that Mel-Spectrogram +
CNN can achieve the best performance in cough recognition
than other methods. For randomly divided datasets, the
correct recognition rate is 98%. It can be seen that the
model can still achieve good recognition performance even
FIGURE 3 | Data components.
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Zhou et al. Cough Recognition

if a variety of different sounds are mixed. The train/test loss
curves are presented in Figure 5.
Experiment on No-Leakage Division
Dataset
Considering that the model needs to cope with the cough sounds
of different people, we add an experiment to estimate the
generalization ability of the model. In this experiment, all the
cough data are augmented, but the cough sound in the training set
and the test set come from totally different collection objects. In
this way, it can detect whether the model has the ability to
recognize the cough sound produced by strange sound sources
effectively.
The train/ test loss curves of no- leakage experiment are
presented in Figure 6 and the experiment result is shown in
FIGURE 4 | The Architecture of the Mel-spectrogram and CNN model.
TABLE 1 | The comparison results of different methods.
Methods Random division recognition task No-leakage division recognition task
Accuracy (%) Recall (%) Precision (%) F1 Score (%) Accuracy (%) Recall (%) Precision (%) F1 Score (%)
Mel-spectrogram + CNN 98.18 99.18 99.28 99.23 95.18 93.33 100 96.55
Mel-spectrogram + BP 94.34 87.50 100 93.33 91.44 93.75 93.75 93.75
MFCC + CNN 97.43 88.88 100 94.12 94.04 100 88.88 94.11
MFCC + BP 96.12 97.19 93.87 97.19 93.45 90.91 100 95.23
MFCC + SVM 95.76 96.99 94.57 95.77 93.29 93.56 91.79 92.67
MFCC + K-means 52.93 42.86 53.09 47.43 50.34 42.44 44.96 43.66
MFCC + Naive-bayes 88.57 95.31 83.83 89.20 78.81 82.43 73.87 77.92
MFCC + LightGBM 95.73 98.46 93.29 95.80 89.89 88.17 89.38 88.77
FIGURE 5 | The loss of the random division experiment. FIGURE 6 | The loss of the no-leakage division experiment.
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Zhou et al. Cough Recognition

Table 1. The no-leakage recognition accuracy is 95.18% and the
F1 score is the highest of all methods. It can be seen that the
model performs well during generalization cough
recognition tasks.
Experiment Based On Other Traditional
Methods
In order to prove the effectiveness of this method, we use several
other methods for comparison.
MFCC
MFCC is an effective method to extract audio features. We use this
method to preprocess the original audio data and then pass it to the
different model. In order to make it suitable for the linear model, in
the experiment, we take the average value on each dimension.
Back Propagation Network
BP is a multilayer feedforward network which has a strong
nonlinear mapping ability. In our experiment, we build a four-
layer BP neural network and the activation is ReLU.
Support Vector Machine
A Support Vector Machine (SVM) is a kind of generalized linear
classifier that classifies data according to supervised learning.
K-Means
The K-means algorithm is an iterative clustering algorithm. Firstly, it
randomly selects Kobjects as the initial clustering center. Then it
calculates the distance between each object and each seed cluster
center and assigns each object to the nearest cluster center.
Naive-Bayes
Naive Bayes is a classification method based on Bayes theorem
and the independent hypothesis of characteristic conditions.
LightGBM
LighGBM is one of the boosting set models. It is an efficient
implementation of the Gradient Boosting Decision Tree (GBDT)
as XGBoost. In principle, it is similar to GBDT and XGBoost.
It uses the negative gradient of loss function as the residual
approximation of the current decision tree to fit the new
decision tree.
All results based on these methods are shown in Table 1, and
we can find that the CNN model is better than these methods in
recognition accuracy and other indicators.
CONCLUSION
In this work, we proposed a cough recognition network (CRN)
based on the CNN model and a Mel-spectrogram. From the
experiments result based on random division and no-leakage
division datasets, we can find that the proposed CRN can achieve
excellent performance in cough recognition. Compared to other
methods, the accuracy of CRN is highest and most of the indexes
are the best. In order to estimate the generalization ability of the
model, we have collected some cough sounds that were not
included in training. We find that the CRN can also recognize
them efficiency. Experiments show that the model can recognize
coughing in complex scenes effectively, and can recognize
coughing with various other sounds correctly, which is good
for cough monitoring in daily life. Cough recognition is a
potential solution for disease management during the COVID-
19 pandemic and reduces epidemic prevention workers’exposure
possibility.
Although the model has achieved good recognition results,
there are still some problems that need to be further solved. For
example, the audio length is now limited to 1 s. When the
intercept position is not right, it may be misjudged.
DATA AVAILABILITY STATEMENT
Publicly available datasets were analyzed in this study. This data
can be found here: https://github.com/karolpiczak/ESC-50 ESC-
50 Dataset http://download.tensorflow.org/data/speech_
commands_v0.02.tar.gz Speech Commands Dataset.
AUTHOR CONTRIBUTIONS
BF proposed the idea of the paper. QZ and JS designed the network
and wrote the manuscript. WD, CW, and SY wrote the code and
analyzed the results. FS and HL helped improve the paper.
FUNDING
This work supported by the National Key Research and
Development Program of China (2017YFE0113200), National
Natural Science Foundation of China (Grant No. 91848206),
Beijing Science & Technology Project (Grant No.
Z191100008019008) and Natural Science Foundation of
university in Anhui Province (No.KJ 2019A0086).
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Zhou, Shan, Ding, Wang, Yuan, Sun, Li and Fang. This is an
open-access article distributed under the terms of the Creative Commons Attribution
License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the
original publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not comply
with these terms.
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