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Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks

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  • Resmonics AG

Abstract and Figures

Ubiquitous mobile devices have the potential to reduce the financial burden of healthcare systems by providing scalable and cost-efficient health monitoring applications. Coughing is a symptom associated with prevalent pulmonary diseases, and bears great potential for being exploited by monitoring applications. Prior research has shown the feasibility of cough detection by smartphone-based audio recordings, but it is still open as to whether current detection models generalize well to a variety of mobile devices to ensure scalability. We first conducted a lab study with 43 subjects and recorded 6737 cough samples and 8854 control sounds by 5 different recording devices. We then reimplemented two approaches from prior work and investigated their performance in two different scenarios across devices. We propose an efficient convolutional neural network architecture and an ensemble based classifier to reduce the cross-device discrepancy. Our approach produced mean accuracies in the range [85.9%, 90.9%], showing consistency across devices (SD = [1.5%, 2.7%]) and outperforming prior learning algorithms. Thus, our proposal is a step towards cost-efficient, ubiquitous, scalable and device-agnostic cough detection.
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Towards Device-Agnostic Mobile Cough Detection
with Convolutional Neural Networks
Filipe Barata, Kevin Kipfer, Maurice Weber, Peter Tinschert§, Elgar Fleisch∗§, Tobias Kowatsch§
Center for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich
Department of Computer Science, ETH Zurich
Department of Mathematics, ETH Zurich
Z¨
urich, Switzerland
Email: fbarata@ethz.ch, kipfer.kevin.j@gmail.com, webermau@student.ethz.ch, efleisch@ethz.ch
§Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen
St. Gallen, Switzerland
Email: peter.tinschert@unisg.ch, tobias.kowatsch@unisg.ch
Abstract—Ubiquitous mobile devices have the potential to
reduce the financial burden of healthcare systems by providing
scalable and cost-efficient health monitoring applications. Cough-
ing is a symptom associated with prevalent pulmonary diseases,
and bears great potential for being exploited by monitoring
applications. Prior research has shown the feasibility of cough
detection by smartphone-based audio recordings, but it is still
open as to whether current detection models generalize well
to a variety of mobile devices to ensure scalability. We first
conducted a lab study with 43 subjects and recorded 6737 cough
samples and 8854 control sounds by 5 different recording devices.
We then reimplemented two approaches from prior work and
investigated their performance in two different scenarios across
devices. We propose an efficient convolutional neural network
architecture and an ensemble based classifier to reduce the
cross-device discrepancy. Our approach produced mean accu-
racies in the range [85.9%, 90.9%], showing consistency across
devices (SD = [1.5%, 2.7%]) and outperforming prior learning
algorithms. Thus, our proposal is a step towards cost-efficient,
ubiquitous, scalable and device-agnostic cough detection.
Index Terms—Cough monitoring; mobile sensing; machine
learning; convolutional neural network
I. INTRODUCTION
Coughing is the most common complaint as to why indi-
viduals seek medical advice [1], [2]. It is mostly known to
be a prominent symptom of the common cold, but it is also
associated with many respiratory diseases including chronic
obstructive pulmonary disease (COPD), asthma, tuberculosis,
gastro-oesophageal reflux, chronic bronchitis and cystic fi-
brosis. Cough is defined by a three-phase expulsive motor
act, starting with an inspiratory effort, followed by a forced
expiratory effort against a closed glottis and ending by an
opening of the glottis and rapid expiratory airflow [1]. Its
characteristic sounds, however, are generated by rapid changes
in airflow caused by the contractions of muscles in the chest
wall, abdomen, diaphragm and larynx [1].
Finding an objective measure of cough frequency in patients
is an undertaking that began in the 1950s and continues
today. This is motivated by the eminent need to assess the
severity of cough and the effectiveness of treatment in patients
with respiratory conditions, and the often found unreliable
assessment of cough based on patient self-reports [3]. To avoid
inaccuracies of patient self-reports and to reduce the patient’s
burden of data collection, automatic cough detection systems
have been proposed to count coughs from audio recordings.
Cough monitoring, or more specifically, cough detection from
audio recordings has been thoroughly investigated in literature
as outlined in the related work section. No cough monitor,
however, is currently the gold standard [1].
The challenge of designing an automated cough detec-
tion system is manifold. Particularly challenging is the rare
occurrence of cough, which demands high specificity from
the cough detection system to avoid false alarms from other
similar and more frequently occurring respiratory sounds such
as throat-clearing, laughter or speech. Moreover, a sensitivity
beyond 90% has been identified to satisfy expert require-
ments [4]. Another challenge is that a cough detection system
must be capable of operating continuously over a prolonged
time frame in order to capture a representative cough fre-
quency with respect to a respiratory disorder, i.e. multiple
hours, a night or even a whole day. Therefore, either the cough
detection system should be connected to the power supply or
the battery life of the system should be sufficiently long. Since
most cough detection systems are envisioned to be operating
in the field under any circumstances, they operate on limited
battery life and therefore energy efficiency is a critical design
criterion [5]. Finally, cough is associated with the most preva-
lent chronic diseases defining a need for a scalable and cost-
efficient cough detection system. In asthma, a chronic disorder
involving the airways and the lungs, coughing is considered
an important symptom because it predicts disease severity [6],
indicates worse prognosis [7], and in particular overnight it has
been identified as a potential biomarker for asthma control [8].
Asthma is estimated to affect 334 million people worldwide
with an estimated increase of asthma patients by 100 million
in 2025 [9]. It has a significant economic impact due to
its prevalence among younger working age groups and loss
of productivity. The current annual costs of healthcare and
productivity loss are estimated to reach EUR 33.9 billion in
the EU [10].
Ubiquitous available ICT, such as smartphones or wearable
devices can be used to unobtrusively monitor patients’ health
condition as these devices are omnipresent and almost always
carried by individuals [11]. Moreover, low-cost smartphones
have sensors able to achieve clinical accuracies, e.g. the
prediction of gait speed in 6-min walk tests, a standard
assessment for COPD or congestive heart failure [12]. Also,
the feasibility of cough detection by smartphone-based audio
recordings has been studied in [5], [13]. However, it is still
open as to whether these detection models generalize well to a
variety of mobile devices to ensure population-wide scalability.
In addition, the varying characteristics of different devices
has been identified in literature as a limiting factor in audio
based machine learning applications [14], [15]. Ultimately,
device-agnostic smartphone based cough detection would not
only guarantee scalability and cost-efficiency, but would also
provide a reproducible measurement of one of the notorious
symptoms of human disease.
Against this background, we address the following research
question in this paper: To which extent can machine learning
based approaches enable device-agnostic mobile cough detec-
tion?
As a primary contribution to the existing body of research,
this work investigates the feasibility of employing machine
learning to predict coughs from audio recordings and evaluates
the predictive power thereof across different devices. It is a
first step towards a scalable cost-efficient case-specific cough
detection system on the smartphone. We first implement
two methods for smartphone cough detection known from
literature [13] and [5]. Further, we propose a convolutional
neural network (CNN) architecture and exploit an ensemble
implementation to further boost the performance in a device-
agnostic preserving manner.
The remainder of this paper is structured as follows. The
following section discusses related work on cough detection
approaches. To answer the research question and to compare
the efficiency of our approach with prior work, we describe
in Section 3 a lab experiment in which we have created a
corresponding dataset with five devices and 43 participants.
We further describe the methodology, where we reimplement
two approaches from prior work and propose an efficient CNN
architecture and its ensemble. In Section 4 we present our
results. Section 5 discusses our findings. Finally, Section 6
summarizes our work.
II. RE LATE D WOR K
Cough detection or more particularly the monitoring of
coughing has been under research and development since the
1950s [25]. Table I gives an overview of related work about
automated cough detection algorithms.
Most of the approaches listed rely on feature extraction.
In conventional machine learning, those features are typically
handcrafted with the goal of reducing the data size, which
is required to describe the prediction problem at hand, in
our case predicting cough from sensor data. Subsequently,
the computed features are used as input for the algorithm to
train and enable the learning of a pattern represented in the
data, such as coughs. This has distinct advantages. First, a
condensed representation of the data requires less memory,
less computation power and ultimately reduce the risk of
overfitting the model to the training samples resulting in poor
generalization to new unseen samples. However, this comes
all with the risk of losing valuable information and limiting
the representation power of the learning algorithm.
By contrast, deep learning, a specific class of machine
learning shifts the complexity of handcrafted feature engi-
neering towards model optimization, since convergence is not
given. This paradigm shift helps taking advantage of additional
available computation power and data [26]. Deep learning ar-
chitectures have come to bolster the state-of-the-art of various
domains, such as speech recognition [27] and image classifi-
cation [28]. They are based on artificial neural networks [29]
and can be characterized as a model consisting of a cascade of
multiple layers of nonlinear information processing [30]. Even
though some of the most prominent cough detection algo-
rithms, which have been employed to conventional condenser
microphone signals [17], [21]–[23] exploit neural network
architectures, they heavily focus on the engineering of features
and still have rather shallow architectures consisting of 2-
4 hidden layers. This may be explained by the difficulty of
collecting large amounts of data from real subjects resulting
in fewer data samples, which limited scaling to a deeper
network. Other approaches have focused on computationally
efficient solutions providing cough detection algorithms for the
smartphone [5], [13]. They all employed conventional machine
learning algorithms, such as random forests and k-nearest
neighbors. Even if the provided sensitivity and specificity
values are comparable or even exceed other approaches their
efforts were not investigated across devices. There is only one
approach, which used two smartphones for the recordings [5].
The devices were placed in different locations, i.e. on the
table or inside the bag/pocket, and evaluated separately with
respect to their position. The large corpus of research in this
field, as well as the repeatedly reported high-performance
values over different datasets, establish the proof of concept
for cough detection. However, it is still open as to whether
these detection models generalize well to a variety of mobile
devices to ensure population-wide scalability.
III. MET HO D
A. Data Collection
We first created a labeled audio corpus for training and
evaluation of our models. The aim of this audio corpus was
to record various voluntary cough sounds, but also some other
sounds identified in the literature as typical examples of sounds
that are confused with cough [24], [31]. Voluntary coughs have
been used in previous literature to show the feasibility of cough
monitors [18], [19], [24], [32] or objectively evaluate the
performance of several sensors for cough detection [31]. The
lab setting followed a similar set-up as in [31], where a person
is sitting (in a quiet environment) at a table on which the
recording device is placed. Consistent with these approaches
Author Recording Device Algorithm Subjects
(Coughs)
Cough
Type Sensitivity Specificity
Coyle et al. 2005
(LifeShirt) [16]
Contact Mic. + Sensor
Array no details available 8 (3645) Reflex 78.1% 99.6%
Barry et al. 2006
(HACC) [17] Lapel Mic. PNN 15 (2000) Reflex 80% 96%
Birring et al. 2008
(LCM) [3] Lapel Mic. HMM 15 (1836) Reflex 91% 99%
Vizel et al. 2010
(PulmoTrack) [18]
Piezoelectric Belt + Lapel
& 2 Contact Mic. no details available 12 (no details
available)Voluntary 96% 94%
Drugman et al. 2011
[19] Contact Mic. ANN 22 (2304) Voluntary 94.7% 95%
Larson et al. 2011
[13] Smartphone Built-In Mic. RF 17 (2558) Reflex 92% 99.5%
McGuiness et al.
2012 (VitaloJak) [20] Piezo Sensor Median Frequency
Threshold
10 (no details
available)Reflex 97.5% 97.7%
Swarnkar et al. 2013
[21]
Matched Pair Low-Noise
Mic. NN 3 (342) Reflex 93.44% 94.52%
Liu et al. 2014 [22] Lapel Mic. GMM-HMM &
GMM-RBM 20 (>2549) Reflex 90.1% 88.6%
Amrulloh et al. 2015
[23] Low-Noise Mic. TDNN 24 (2090) Reflex 93% 98%
Amoh et al. 2016
[24]
Wearable Sensor /
Contact Mic. CNN & RNN 14 (627) Voluntary 87.7% 92.7%
Monge-Alvarez et al.
2018 [5]
(2x) Smartphone Built-In
Mic. k-NN 13 (no details
available)Reflex 88.51% 99.7%
TABLE I: Overview over prior literature on automated cough detection algorithms. Note: Microphone Mic., Probabilistic
Neural Network PNN, Hidden Markov Models HMM, Artificial Neural Network ANN, Random Forest RF, Neural Network
NN, Gaussian Mixture Models GMM, Restricted Boltzmann Machine RBM, Time Delay Neural Network TDNN, Convolutional
Neural Network CNN, Recurrent Neural Network RNN and k-Nearest Neighbor k-NN.
we conducted a lab study to investigate the feasibility of
generalizing cough detection to a variety of mobile devices.
In addition, two recording distances were used, to mitigate
the influence of a possible distance bias. The devices were
placed on the table, once directly in front of the chair, and
once shifted to the left by 1m, with a chair-table distance
of 15cm. The data collected in a lab setting followed the
study protocol outlined in [33]. Participants were instructed
to voluntarily cough 20 times at two different distances while
being audio recorded by five different devices: HTC M8,
Samsung S6, Apple iPhone 4, Google Nexus 7 tablet and
one studio microphone Røde NT1000). Also, different control
sounds such as laughter, throat clearing, forced expiration,
and speech were recorded in the same manner. The mobile
devices were set up to record using the standard audio
recording application to increase external validity of the audio
recordings. They were also connected to an audio interface
(Focusrite Scarlett 18i20), which again was connected to a
computer to control the recording by means of Audacity, an
audio software for multi-track recording and editing as can be
seen Figure 1. The studio microphone was connected to the
audio interface too, but the device-specific driver defined its
settings. The devices were turned on to record all the sounds
at the preset sampling frequency and bit rate, namely 44.1 kHz
with 16 bits, respectively.
We employed second-hand devices, which range among
the most popular models in Europe in 2016 covering the
leading mobile operating systems Android and iOS. Also,
the devices differ due to different microphone specifications,
processing hardware, usage and service life. To investigate the
recording quality of each device, we compared the spectra of
the measured signals of the different devices to the spectrum
of the signal measured by the studio microphone. In detail, this
means we computed the power spectral density (PSD) [34] and
used the PSD of the studio microphone as the reference. We
computed the mean squared error (MSE) of the PSD of the ref-
erence measurement and the smartphone/tablet measurement
over all measured coughs. We subsequently normalized the
MSEs of each cough with respect to the coughing participant.
The average including the standard deviation of the MSE for
each measuring device can be seen in Figure 2. Higher means
of the MSE for HTC M8 and Google Nexus 7 recordings
indicate a higher deviance from the recordings of the studio
microphone Røde NT1000 and therefore suggesting a lower
quality in comparison to the recordings of Apple iPhone 4 and
Samsung S6. The audio data were human annotated, labeling
all acoustic events as one of the five categories cough, speech,
throat clearing, laughter and forced expiration. All acoustic
events were annotated by a single person. Calculating inter-
rater reliability was considered unnecessary due to the highly
standardized lab setting with virtually no interfering noises.
B. Evaluation
In order to answer our research question we investigated
two scenarios. First, we trained and tested our models on all
devices, but tested on unseen subjects in our database. This
emulates the scenario of deploying a mobile cough detection
model on a known device. The evaluation is then conducted
on each device separately, since our recordings consist of
audio events recorded over 5 devices at the same time. In
the second scenario we trained on samples of a subset of 4
devices, but tested on the remaining unseen device with unseen
Fig. 1: Data collection setup.
HTC M8
Samsung
S6
Apple
iPhone 4
Google Nexus
7 tablet
0.46
0.48
0.50
0.52
0.54
MSE
Fig. 2: PSD comparison of the different recording devices to
the studio microphone. The mean and the standard deviation
refer to the mean squared error (MSE) computed over the
different cough recordings made by the smartphones/tablet and
the recordings made by the studio microphone.
participants. Analogously, as in the first scenario we then eval-
uated it with respect to all devices individually. This scenario
emulates the case of deploying a model on a device which is
unknown to the model builder. In deep learning the split into
two or three sets for training, validation and evaluation of the
architectures is favored over other approaches, such as cross-
validation, due to the long training phases of the models. This
comes at the risk of overfitting to the specific dataset and
the lack of generalizability to unseen samples. To mitigate
that effect we split our sets into disjunct training, validation
and test datasets containing different participants. The models
were then trained iteratively on the training set, but tuned and
selected based on the prediction results on the validation set.
After the model was chosen, it was again trained on the merged
training and validation set and its final prediction results
were then reported on the unseen test set. The approaches
proposed in this work were implemented using Tensorflow,
an open-source software library for training neural networks
in Python [35]. Also, TensorFlow-Slim [36], a lightweight
library on top of Tensorflow for defining and training complex
Precutting
/Annotation Preprocessing Network
Training Evaluation Results
Audio
Signals
(a)
Extracting
Audio Event Preprocessing Cough
Detection
Audio
Signals
Cough
Non-
Cough
(b)
Fig. 3: (a) Machine learning pipeline: Describes the learning
of a model from audio signals. (b) Cough detection pipeline:
Describes the inference of cough from audio recordings.
models, was used. All the models were trained on the cluster
infrastructure of ETH Zurich. The approaches of prior work
were implemented in Python using the machine learning
library Scikit-learn [37]. Finally, benchmarking of memory
usage, floating-point operations and number of parameters was
accomplished by using the Model Benchmark Tool provided
by TensorFlow.
C. Classifiers for Mobile Cough Detection
To investigate the research question, we use previous ap-
proaches which have been developed for mobile cough detec-
tion as baselines. We reimplemented the most cited [13] and
the most recent approach [5] with regard to cough detection
using a smartphone. These reimplementations are best efforts
and were subject to further optimization. Also, we introduce
an architecture inspired by recent research [38], where we
developed an efficient CNN-based model. We further introduce
a leave-one-device-out ensemble to foster device-agnostic clas-
sification performance. Detailed descriptions of preprocessing,
network training, and implementation are provided in the fol-
lowing subsections. An overview of the corresponding blocks
of the machine learning pipeline is shown in Figure 3a. In
this work we focus on building models for cough detection.
These models can be deployed in an automatic cough detection
pipeline as shown in Figure 3b. Moreover, to make cough
detection more efficient, most approaches introduce a step
before the detection, where the stream of acoustic data is
segmented into time frames and subsequently analyzed to
determine whether the time frame at hand may contain any
significant data. This decision is made by computing rather
simple features such as the energy of the time frame and
the comparison to a predefined threshold. In this manner
time frames containing silence are discarded. The remaining
time frames are processed further to serve as input for the
prediction model and ultimately result in a cough/non-cough
classification. We omit the description of such a step of
extracting a relevant audio event since it has been sufficiently
addressed in previous work [13], [24]. Thus, the preprocessing
step in both pipelines in Figure 3a and 3b can be used
interchangeably.
D. Approaches from Prior Work
1) Random Forest with PCA for Feature Selection: For the
first method, we implemented the approach outlined in [13].
In the preprocessing step, we first extracted a window of
size 320 ms from the original raw audio signal and then
employed min-max normalization in order to scale it into
the range [-1,1]. We then generated a feature representation
of the audio data and subsequently trained a random forest
classifier on these features. Specifically, the model is generated
by first running PCA with ten components on the vectorized
magnitude spectrograms yielding the first ten features for the
model. As in [13], we computed the residual error between
the reconstructed and original vectorized spectrograms, and
additionally the mean decibel energy for both the entire
reconstructed spectrograms and for the FFT coefficients above
11kHz, and below 11kHz, yielding four more features. After
the cough model is generated, we train a random forest
classifier with 500 trees. We further optimized on the maximal
number of levels in each decision tree and the maximal
number of features resulting in a maximal depth of 14 and
10 features, respectively. The parameters, maximal number
of levels (14), maximal number of features (10), number of
trees (500), window size (320 ms) and the decision point in
frequency (11 kHz) were result of optimization and differ from
the approach in [13].
2) k-nearest Neighbor with Local Hu Moments as Robust
Features: For the second method, we implemented the ap-
proach outlined in [5]. In the preprocessing step a Kaiser
window is used to separate the signal into different frames.
From each frame the PSD is then estimated and normalized.
Subsequently, the logarithm of the spectral energies for every
window in a series of bands defined by a filterbank in the
Mel scale is computed. The resulting spectral energy matrix
is then subdivided into block matrices, from which the first Hu
moment is then computed. This causes a reduction in the size
of the column dimension in comparison to the the original
spectral energy matrix, which is determined by the size of
the block matrices. Finally, the discrete cosine transform is
computed for each row and the first and last coefficient are
discarded, resulting in the final feature representation. All
feature sets were normalized to have zero-mean and unitary
standard deviation. These features are then fed into a k-
nearest neighbor classifier (kequals 3), with standardized
Euclidean distance as distance metric and with the inverse of
the distance as weighting function. The parameters, window
length (400 ms), sampling frequency (22.05 kHz), number of
neighbors (3) were result of optimization and differ from the
approach in [5].
E. Approach Proposed in this Work
1) Preprocessing: Cough can be quantified in many dif-
ferent ways. The most intuitive method of counting cough
is counting the characteristic explosive sounds [1]. Although
obstructed airflow can produce noises with frequencies up to
20 kHz, cough characteristic and most energetic sounds are
associated with frequencies below 10 kHz [39]. We, therefore,
reduce the size of the data and optimize on the training time
of our algorithms by downsampling the signals to 22.05 kHz
after applying an anti-aliasing filter. Furthermore, in order to
focus on the classification of the explosive characteristic, we
computed the maximum amplitude of the extracted acoustic
events in the time frames and subsequently extracted 325
ms around the maximum yielding 650 ms of the signal.
The time frame of 650 ms corresponds approximately to the
average total length of a cough [39] and was the result of
hyperparameter optimization after employing grid search on
the value range [0.05s, 1s]. We thus argue that 650 ms are
enough to capture the explosive phase of a cough reflex. The
extracted signal was then standardized by employing min-max
normalization. Subsequently, a mel-scaled spectrogram was
computed with 16 bands, 112 samples between successive
frames and a 2048 point FFT. Mel-scaled spectrograms are
visual representations of sound with respect to frequency
and time. The frequency domain, however, is mel-scaled
to represent the human perception of tone. Consistent with
prior work we use mel-scaled spectrograms as inputs for our
CNN architectures. Mel-spectrograms have been investigated
thoroughly in literature and especially in conjunction with
CNNs, they have been reported to perform best in comparison
to other time-frequency representations [40].
2) CNN Architecture: Deep Learning methods have im-
proved the state-of-the-art in various machine learning do-
mains [26]. Especially their good generalization behavior in
practice is among the main reasons for their adoption [41]. In
recent years, CNNs have been one of the most frequently used
architectures in the context of computer vision [42]. CNNs
have proven effective in image classification, but have also
shown promising results for audio applications, in particular
in conjunction with spectrograms for audio event detection
(e.g. environmental sounds of birds, violins, airplanes or foot-
steps) [43]. Typical CNN architectures consist of alternating
convolution and max-pooling layers followed by a small
number of fully connected layers [44]. At the heart of CNNs
lies the convolutional layer, which exploits the translation
invariance property of convolutions, i.e. it can abstract from
the position where the object to be identified is located in the
picture (or mel-spectrogram as in our application).
We therefore exploit the generalization behavior of deep
learning, in particular CNNs to reduce the device-specific dif-
ferences in our recordings. Among the most prominent CNN
architectures is VGG [38], which was able to significantly
improve image recognition accuracies by pushing depth to
19 layers and increasing the amount of convolutional layers.
In our experiments, we exploited the same approach and
optimized by iterating over the total amount of convolutional
layers. Best results were achieved with a depth of 5 convolu-
tional layers. Furthermore, as mentioned in the introduction,
energy efficiency is a critical design criterion. We therefore
developed our CNN cough detection architecture with two
additional modifications in order to reduce the computational
expenses of the CNN architecture. First, we introduced max
global pooling, which combines the maximum of each feature
map directly into the output layers, replacing the expensive
fully connected layers. This operation, which may be seen as
a simplification, brings the advantage that it does not introduce
new parameters. In our experiments we used max global
pooling, which uses maximization instead of averaging [45],
which ultimately resulted in a better performance on the
validation set. Second, we replaced the regular convolutions
with depthwise separable convolutions [46]. As a consequence,
the number of calculations is reduced in comparison to a
regular convolution and thus the efficiency of the architecture
is improved. To illustrate this reduction we contemplate the
following example, assume we have an image of 12 ×12
pixels, which we want to apply 64 3×3convolutional kernels
with a stride of 1 and zero padding. The outcome is a
new image of 10 ×10 pixels with 64 channels equivalent
to moving a 3×3kernel 10 ×10 times over 64 channels
resulting in a total of 3×3×10 ×10 ×64 = 57.6k
calculations. In contrast, a depthwise separable convolution
introduces an additional 1×1kernel separating the convolution
into a depthwise 3×3×1×1and a pointwise 1×1×64
convolution. This results in 3×3×10 ×10 = 0.9kplus
10 ×10 ×64 = 6.4kcalculations yielding a total of 7.3k
calculations. Further parameters which have been subject to
hyper-parameter optimization on the validation set are the
number of channels and the size of the convolutional filters.
The optimum was reached for 64 channels. We further used
dropout with a rate of 50%, which is a regularization technique
to prevent overfitting [47]. The resulting parameters such as
filter sizes and the whole architecture can be inspected in
Figure 4b.
3) Network Training: In our work, the networks are trained
using the stochastic optimization method Adam, which has
been successfully applied in practice [48]. For our architec-
tures, the learning rate lied in the range of [0.001, 0.007] and
the batch size was set to 64. The learning rate and batch size
were found by running the model several times with different
configurations to find the optimal training hyper-parameters.
Furthermore, weight initialization was accomplished by em-
ploying Xavier initialization [49] throughout the different
architectures. We further applied techniques which have been
reported to have a positive effect on stochastic gradient descent
such as gradient clipping [50] and adding gradient noise [51].
We added gradient noise with a variance of 0.009.
4) Ensemble Method: In machine learning, ensemble meth-
ods use the power of different models in order to improve
the predictive performance over one individual model [52].
Among the most popular methods lies bootstrap aggregating
(bagging) [53], where each model of the ensemble is trained on
a randomly drawn subset of the training set. This promotion of
model variance has been shown to often improve performance
of amalgamated models such as CNNs [54]. In particular, it
has been shown empirically that by averaging the outputs of
multiple estimators, a better estimate with less generalization
error is obtained [55]. We hope to exploit this property of
ensembles in a device-agnostic manner by employing bagging
to our CNN architecture, not only to train on a subset of the
training set, but also on a subset of the devices used for the
recording. If an ensemble learning algorithm is sensitive to
perturbation on training samples, then the individual models
are dissimilar, and thus combining them will help improve the
generalization performance [56]. Analogously, if constraining
the training on randomly choosing samples of a subset of
the recording devices introduces enough variance, then we
may be able to reduce the generalization error across devices.
Regardless of the number of devices, we propose the bagging
algorithm as in Algorithm 1, where the inner loop corresponds
Algorithm 1 Device-agnostic bagging
Input: I(an inducer, responsible for creating the model),
T(the number of iterations),
S=S1, . . . SK(the training set composed of recordings
of different devices K),
µ(the subsample size)
Output: :Mt,k;t= 1, . . . , T ;k= 1, . . . , (K
n)
1: k1
2: repeat
3: DkSample one set of ndevices of (K
n)combina-
tions without replacement.
4: S{Dk}Fetch training samples related to this sample
of devices Dk
5: t1
6: repeat
7: Sk,t Sample µinstances from S{Dk}with re-
placement.
8: Build classifier Mk,t using Ion Sk,t
9: t+ +
10: until t > T
11: k+ +
12: until k > (K
n)
to the common bagging algorithm. Since for our recordings we
had a set of five devices (K= 5) at our disposal, we optimized
on the amount of training data available to train each of our
models (I= CNN architecture) choosing the other parameters
accordingly as n= 4 or 3,µ=|S{Dk}|and T= 1, resulting
in an ensemble of 5 or 4 models each trained on samples
of incongruent sets of 4 or 3 devices with respect to our two
scenarios. Figure 5 depicts the case of the ensemble consisting
of 5 models.
IV. RES ULT S
A. Dataset
Overall, 43 healthy participants (31 female, 12 male) were
recruited. Their age ranged from 18 to 45 with a mean value
of 26 (SD: 6). This resulted in a total of 6737 cough, 3985
laugher, 3695 throat clearing, 731 speech and 443 forced
expiration audio signals, which represents a vast number of
cough samples and participants in comparison to former stud-
ies (see Table I). Further, training, validation and test set were
composed in the following way. Out of 43 participants, we
drew 11 at random, their audio samples were then included in
the test set. From the remaining 32 participants, 5 participants
were drawn at random and included in the validation set. The
result fulfilled roughly the ratio of a 60/15/25 data split.
max pool 1/2
conv 3x3, 64
conv 3x3, 64
max pool 1/2
conv 3x3, 128
conv 3x3, 128
conv 3x3, 256
conv 3x3, 256
conv 3x3, 256
conv 3x3, 512
conv 3x3, 512
conv 3x3, 512
max pool 1/2
conv 3x3, 256
conv 3x3, 512
max pool 1/2
fc 4096
fc 1000
fc 4096
conv 3x3, 512
conv 3x3, 512
conv 3x3, 512
max pool 1/2
conv 3x3, 512
(a) VGG-19 architecture [38]
dep sep conv 1x7, 16
dep sep conv 1x5, 64
dep sep conv 3x3, 64
dep sep conv 3x3, 64
dep sep conv 3x3, 64
fc 2
global max pool
max pool 1/2
max pool 1/2
max pool 1/2
max pool 1/2
(b) Proposed CNN architecture
Fig. 4: CNN architectures: The annotations ”dep sep conv 1×7,16” represent a depthwise separable convolutional layer with
a1×7convolutional filter and 16 channels and ”fc 2” a fully connected layer with two outputs, respectively. Max pooling is
abbreviated as ”max pool”.
Fig. 5: Proposed bagging algorithm for five devices (K= 5)
and parameters n= 4,µ=|S{Dk}|and T= 1.
B. Performance Results
The results of our evaluations are listed in Tables II - III.
Table II gives an overview of the device-specific accuracies
for the different devices. Table III describes the results across
devices and shows the mean and the standard deviation of
the following metrics, sensitivity (SENS), specificity (SPEC),
accuracy (ACC), Matthews correlation coefficient (MCC),
precision (PPV) and negative predictive value (NPV):
SENS =T P
T P +F N ,
SPEC =T N
T N +F P ,
ACC =TP +T N
T P +T N +F P +F N ,
MCC =T P ·T N F P ·F N
(T P +F P )(T P +F N )(T N+F P )(T N +F N ),
PPV =T P
T P +F P ,
NPV =T N
T N +F N ,
where T P, F P , T N and F N denote the number of true and
false positives and true and false negatives, respectively.
In Scenario I, the training was done on all devices and
tested on unseen participants. We observe that our proposed
ensemble architecture achieves not only the best mean accu-
racy of 90.9% when evaluated on all devices but also when
evaluated on a per device basis. Only with comparable results
to the single CNN architecture in terms of NPV and SENS.
The two approaches from prior work did not attain the same
levels of performance with respect to our metrics. The random
forest based classifier outperforms the k-NN based classifier
in terms of our metrics achieving an overall mean accuracy
of 83.5%. The biggest discrepancy lies in the MCC values,
which is a balanced metric used to measure the quality of a
binary classification, with k-NN having the lowest value with
46.8% and the ensemble having the highest value with 81.7%.
Further, the standard deviation of the SENS, ACC, MCC and
NPV values is reduced for the CNN based architectures in
comparison to the other approaches. We also observe that the
quality of the devices’ recordings is reflected in the results,
meaning the accuracy values on HTC M8 and Nexus 7 tablet
recordings are lower in comparison to the accuracy values of
the other higher quality devices with respect to our reference
(column accuracy means of Table II for Scenario I from
left to right: 86.7%, 85.7%, 86.2%, 80.5%, 81.2%). Figure 6
concludes the results section of the first evaluation, showing
receiver operating characteristic curves for all four approaches
on all devices. The greatest area under the curve is again
achieved by the ensemble (0.96), followed by the single CNN
(0.95), the random forest (0.91) and the k-NN classifier (0.79).
In Scenario II, the training was done on all devices except on
the one annotated as column name and then evaluated on the
recordings of the unseen device of unseen participants. Again
the highest mean accuracy is achieved by the ensemble with
87.6%. The accuracy of the ensemble is highest on all single
devices with exception of the HTC M8 recordings, where it is
outperformed by the single CNN architecture. The ensemble
further attains the best performance in the SPEC, ACC, MCC
and PPV values. As in Scenario I, the random forest classifier
Class. Røde iPhone Samsung HTC Nexus 7
NT1000 4 S6 M8 tablet
(ACC %) (ACC %) (ACC %) (ACC %) (ACC %)
SCENA RIO I :
Approaches from Prior Work
RF with PCA [13] 88.0 84.8 87.2 79.5 78.2
k-NN with Hu Moments [5] 77.5 77.2 73.4 66.3 68.3
Approach Proposed in this Work
CNN with Mel Spec 89.5 89.7 91.5 87.1 88.7
Ensemble CNN 91.7 90.9 92.8 89.2 89.7
SCENA RIO I I:
Approaches from Prior Work
RF with PCA [13] 85.0 84.7 85.3 75.6 73.1
k-NN with Hu Moments [5] 72.2 76.5 63.6 60.9 63.7
Approach Proposed in this Work
CNN with Mel Spec 83.6 85.3 89.3 86.2 84.6
Ensemble CNN 86.8 89.8 91.0 85.6 84.6
TABLE II: Device-Specific Classification Results
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
0.0
0.2
0.4
0.6
0.8
1.0
True Positive Rate
Receiver Operating Characteristic Curves
k-NN with Hu Moments (AUC = 0.79)
RF with PCA (AUC = 0.91)
CNN with Mel Spec (AUC = 0.95)
Ensemble CNN (AUC = 0.96)
Fig. 6: Receiver operating characteristic curves with corre-
sponding area under the curve values (AUC) for the four
approaches investigated in Scenario I.
surpasses the k-NN classifier in terms of our metrics with a
mean accuracy of 80.7 % and only remains second to the
CNN architectures. We can further observe that the quality
of the recordings is reflected in the results (column accuracy
means of Table II for Scenario II from left to right: 81.9%,
84.1%, 82.3%, 77.1%, 76.5%). Finally, we can observe a drop
in mean performance and an increase in standard deviation for
Scenario II in comparison to Scenario I, in particular for the
SENS, ACC, MCC and NPV values across all devices.
V. DISCUSSION
A. Main Results
Given the dataset consisting of the same acoustic events
recorded over five different recording devices, our results
indicate that the difference in quality of the recordings matter
and are the reason for the fluctuating performance values
across devices. Those discrepancies are mitigated and can
be made negligibly small at high performance values by our
proposed architectures. However, we need to point out that
those discrepancies are inevitably greater when the device is
unknown to the model builder. Our efforts towards reducing
the generalization error across devices by employing deep
learning in form of a CNN architecture and its ensemble
variation were successful and performed better in contrast
to the established approaches in literature. The ensemble is
especially remarkable, since the increase in performance is
explained by the introduction of variance by alternating on the
set of devices to which the model was trained. As shown in
Figure 2 the devices differed in recording quality with respect
to our reference.
B. Practical Implications
In the development of the CNN architecture we strove for
computational efficiency, resulting in a model with 17480
parameters where 1.232 MB memory and 10.74 million
floating point operations are required for its execution. This
raises the question of how to best employ the developed
architectures into practice. Due to large computational power
and memory requirements, deep learning architectures are
typically deployed on cloud computing systems. In the case
of smartphone-based cough detection, the smartphone contin-
uously records audio signals and loads them into the buffer
where it decides if the extracted audio event is relevant. If so,
the data is transferred to the cloud in order to be classified.
This brings the issue of additional privacy requirements, since
audio signals may contain voice and thereby be privacy-
sensitive information.
Continuous advances in mobile hardware make the deploy-
ment of deep learning models on the smartphone possible.
For instance, the iPhone X is advertised to have 3GB RAM
and on top of that a neural processing unit able to execute
600 billion floating-point operations per second. Not only
efforts towards more powerful hardware but also towards
achieving a more efficient deep neural network inference
engine have been increasingly made [57], [58]. In a self-
conducted experiment we deployed our model on a new
Samsung Galaxy A3 (2017) smartphone with an Octa-core 1.6
GHz Cortex-A53 central processing unit and a battery capacity
of 2350 mAh. For that purpose we developed an Android
app, which continuously fills a 0.65s long audio buffer, from
which a mel-scaled spectrogram is computed and classified
with our pre-trained single CNN model using TensorFlow
Lite, an open source deep learning framework for on-device
inference [57]. When a cough is detected, the occurrence is
counted and the total number is displayed. The pre-trained
model was frozen in our Python development environment and
converted to the TensorFlow Lite format before integration
in the assets folder of the Android app. In this six hour
long experiment, we compared the energy consumption of the
developed app running our model in the background on a fully
charged device in comparison to the energy consumption of
the same device with same battery status without app. This
experiment yielded a remaining percentage of battery charge
of 75% and 95% for device with and without app, respectively.
This results in an hourly average of 78.3 mA for the app
Class. SENS SPEC ACC MCC PPV NPV
(mean ±SD %) (mean ±SD %) (mean ±SD %) (mean ±SD %) (mean ±SD %) (mean ±SD %)
SCE NARI O I:
Approaches from Prior Work
RF with PCA [13] 86.7±5.5 80.3±4.4 83.5±4.0 67.2±8.2 83.4±4.4 83.8±7.5
k-NN with Hu Moments [5] 81.2±7.3 65.6±3.3 72.5±4.6 46.8±10.1 65.9±3.3 80.7±8.6
Approach Proposed in this Work
CNN with Mel Spec 91.7±2.8 86.7±3.1 89.3±1.6 78.6±3.2 88.8±3.0 90.0±3.7
Ensemble CNN 91.7±3.1 90.1±3.5 90.9±1.5 81.7±3.0 92.0±3.2 89.5±4.2
SCE NARI O II:
Approaches from Prior Work
RF with PCA [13] 83.3±8.0 79.5±6.7 80.7±5.3 61.9±10.7 83.0±7.8 78.1±12.6
k-NN with Hu Moments [5] 73.6±10.8 62.3±4.3 67.4±5.9 35.4±13.8 67.1±6.5 67.9±16.0
Approach Proposed in this Work
CNN with Mel Spec 86.7±5.9 6.4±7.2 85.8±2.2 72.0±3.9 88.8±6.9 82.2±10.6
Ensemble CNN 86.5±6.6 90.5±4.8 87.6±2.7 75.5±5.4 92.8±4.6 81.2±10.2
TABLE III: Classification Results across Devices
running in the background on the respective device. Even if
this implementation can be made more efficient by introducing
a step before classification discarding time frames containing
silence, it shows the feasibility of using our model on-device
over a prolonged period of time.
The results of this work demonstrate advantages in favor
of the proposed CNN architectures, meaning higher predic-
tion performance and smaller inter-device variability. We,
therefore, argue that our architectures can enable device-
agnostic mobile cough detection. The trade-off between the
established approaches from literature, however, lies in the
computational efficiency and thus energy consumption. The
application of such a model, as shown in our experiment,
may although depend on the battery life of the device. In
stationary settings, where the person to be monitored lies flat
(for example, in a hospital bed or overnight) a scenario could
be envisioned where the smartphone is continuously plugged
in. In more dynamic settings, where recharging of the device
is not possible, a hybrid solution between the ensemble and
the single CNN architecture may be considered assuming a
drop in performance is tolerated. In conclusion, our proposed
architectures fulfill the requirement of scalability by providing
a high device-agnostic detection performance across devices.
They may further find their way into practice and being
deployed as a cloud- or client-based solution.
C. Limitations
The obvious limitation of this study is that only vol-
untary coughs were used to examine the performance of
the different approaches across devices. Even if voluntary
coughs have already been used to show the feasibility
of cough monitors [18], [19], [24], [32], reflex coughs would
further allow a disease specific analysis of the performance
values. Another limitation stems from the laboratory setting of
the study, which limits the generalizability of the performance
metrics. It is not clear whether the exact same performance
patterns would emerge in settings that resemble a users
everyday life. However, due to high performance values of
our proposed architectures specifically in devices with lower
quality recordings, a reasonable argument can be made that
our architectures would still perform consistent across devices
for reflex coughs in a different setting.
Finally, we would like to emphasize that our results can
be enhanced by introducing a wider array of possible non-
cough sounds, thereby increasing the amount of training data,
yielding more powerful classifiers [59]. Moreover, transfer
learning [60] and data augmentation [61] are further tech-
niques, which can help improve the accuracy of CNN-based
classifiers.
VI. CONCLUSIONS & FUT UR E WOR K
This paper contributes to mobile cough detection by an-
alyzing the performance of various mobile cough detection
classifiers and their inter-device variability. We found that
the proposed CNN architecture was less prone to variability
across devices at higher performance values when compared
to approaches from literature [5], [13]. We further bolstered
performance by employing an altered version of bagging to
the developed architecture. We, therefore, conclude that our
architectures can enable device-agnostic cough detection by
means of a smartphone, however the quality of the recording
devices remains a limiting factor. Against the backdrop of
these results, this work represents a very first step towards
scalable, low-cost, ubiquitous, accurate and device-agnostic
cough detection algorithms.
Our current work involves the collection of nocturnal data in
a longitudinal field study with asthmatics and the assessment
of the methods described in this paper with respect to asth-
matic coughs. This is motivated by the number of nocturnal
coughs which could be exploited as an objective assessment
of asthma control [8], thus providing a scalable cost-efficient
marker for asthma.
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... Moreover, by using consumer-centric devices such as smartphones, which are increasingly available in developing countries, our proposed system allows for a scalable and cost-effective ward monitoring tool that can be used in low-and middle-income countries. This work builds upon our prior research [19,27], where we developed cough detection models based on coughs from asthmatic patients. We evaluate the proposed system with nine patients hospitalized for lower respiratory tract infection (three infected with SARS-CoV-2) at night (i.e., 11 pm -7 am). ...
... These six windows are centered around the six maximum absolute amplitudes of the signal, which are spaced at least half a window apart in the audio file. Cough detection is based on an ensemble Convolutional Neural Network (CNN) developed and trained on asthmatic cough data [19,27]. The detection step follows the procedure previously reported by Barata et al. [19]. ...
... Second, we only used data recorded with a specific smartphone model. We have shown that noisy or poor quality recordings from a different device can have a negative impact on the performance of the classifiers [27]. Third, the cough type and frequency encountered in the data may limit the generalizability of our results, as we have only performed our validation in data from hospitalized patients with pneumonia overnight. ...
... Although all detection studies aim ultimately at maximizing the performance of a detector, some focus more specifically on methodological aspects, possibly at the cost of lower overall performances: the robustness to the data sampling frequency and noise [119][120][121][122], the implementation on a smartphone [123][124][125][126][127], the relevance of the recording sensors [27], the non-intrusiveness of the recording setup [128], the deployment of a system in a real home environment [129], the real-time detection using wearable sensors [130,131], the time variations of the signal [132], the discrimination of individuals [133], the privacy preservation [134,135], the optimization of features [68,136], the tuning of parameters [137], the refinement via a confidence factor [138] and the ability to track a disease [14,139]. Finally, a few studies limit the non-cough events to specific events: speech [140], sleeping movements [123], snoring [74,123,141,142] and sneezing [138]. ...
... For the studies concerned with such distinction, 52% of them chose a subject-related acquisition system and 48% a room-related one. In the fix systems, the most frequent solution is to use a microphone or an MP3 recorder ( [30,33,58,68,73,80,87,90,91,93,94,97,99,101,104,110,116,128,129,139,149,150,[160][161][162]169,[172][173][174]178]), or more recently a smartphone as a microphone ( [47,48,[52][53][54]59,70,74,81,84,85,89,96,102,103,112,113,[120][121][122][123]126,135,141,142,171,172]). The subject-attached recording system aims rather at recording during several hours to several days. ...
... A special type of features are the custom features, sorted in the "Deep learning raw data" category. They correspond to the rising field of deep learning ( [29,[45][46][47][48][49][50]52,[54][55][56]78,80,[84][85][86][87]110,126,129,137,143,144,[148][149][150]154,155,157,171,172,183,[185][186][187]). Deep neural networks take as input large raw data, such as spectrogram images, and provide as output or intermediate results a limited set of machine-interpretable values containing pertinent information, the features. ...
Article
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Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.
... detection have achieved sensitivity and specificity values of over 90% [10]. More recent approaches have also employed mobile technologies such as smartphones and wearables for cough detection [11], [12]. This is motivated by the widespread use of mobile technologies, which have the potential to meet the health monitoring needs of the ever-growing number of chronic respiratory patients [13]. ...
... Although the large amount of research conducted in this field shows that proof of concept for cough detection is well established [12], [14], one problem, in particular, remains largely unresolved for contact-free cough detection. That is, to determine to whom a given cough belongs. ...
... Finally, in this paper, we focus on identifying and verifying patients from cough sounds and assume that cough detection has been reliably achieved. We acknowledge that complex practical challenges were left out by bypassing the detection step and refer the reader to our previous works on this topic [12], [16]. ...
Article
Full-text available
Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.
... x (3) x (4) x (2) x (1) x (5) x (6) x (7) x 1 ...
... This application involves data points that represent audio recordings with the fixed duration of three seconds. These recordings are obtained via smartphone microphones and used to detect coughing [7]. ...
... Clustering (4) x (2) x (1) x (5) x (6) x (7) x g x r Figure 8.1: Each circle represents an image which is characterized by its average redness x r and average greenness x g . ...
Chapter
Full-text available
... x (3) x (4) x (2) x (1) x (5) x (6) x (7) x 1 ...
... This application involves data points that represent audio recordings with the fixed duration of three seconds. These recordings are obtained via smartphone microphones and used to detect coughing [7]. ...
... Clustering (4) x (2) x (1) x (5) x (6) x (7) x g x r Figure 8.1: Each circle represents an image which is characterized by its average redness x r and average greenness x g . ...
... Spectral analysis): (Rocha, 2017), (Botha, 2018), (Vizel et al., 2010), (Monge-Alvarez et al., 2019b), (Ferrari et al., 2008), (Salmi et al., 1988), (Abaza et al., 2009), (Murata et al., 1998), (Hsu et al., 1994), (Doherty et al., 1997), (Amoh and Odame, 2016), (Monge-Alvarez et al., 2019a), (Moshou, 2001), (Abaza et al., 2009), (Guarino, 2005), (Belkacem, 2020), (Amrulloh et al., 2015), (Pramono et al., 2016), (Windmon et al., 2019), (Carpentier et al., 2018), (Sharan et al., 2019), (Casaseca-De-La-Higuera, 2015), (Rocha, 2020), (Dubnov, 2020), (Xu et al., 2020), (Orlandic, 2020), (Balamurali, 2020), (Chaudhari, 2020), (Subirana, 2020) (Highest accuracy: 94% for female and 97% for male coughs (Abaza et al., 2009)) -Classification step (e.g. Neural networks): (Parker et al., 2013), (Charles, 2020), (Shi et al., 2018), (Monge-Alvarez et al., 2019b), (Moshou, 2001), (Shin et al., 2009), (Charles, 2020), (Abeyratne, 2013), (Amoh and Odame, 2016), (Monge-Alvarez et al., 2019a), (Kakabutr, 2017), (Swarnkar et al., 2013), (Ali et al., 2020), (Derraz, 2020), (Larson, 2011), (Rocha, 2017), (Botha, 2018), (Amrulloh et al., 2015), (Pramono et al., 2016), (Windmon et al., 2019), (Carpentier et al., 2018), (Sharan et al., 2019), (Larson et al., 2012), (Zhuang et al., 2010), (Casaseca-De-La-Higuera, 2015), (Barata, 2019), (Rocha, 2020), (Hoyos-Barcelo et al., 2018), (Dubnov, 2020), (Khomsay, 2019), (Belkacem, 2020), (Shuja, 2010), (Augustinov, 2020), (Pal and Sankarasubbu, 2020), (Xu et al., 2020), (Pinkas et al., 2020), (Bagad et al., 2020), (Balamurali, 2020), (Chaudhari, 2020), (Alsabek, 2020), (Subirana, 2020) (Highest accuracy: 99% for used model (Charles, 2020)) • Chemical tests in cough discrimination. ...
... Undersampling down to 400 Hz resulted in the sensitivity and specificity values remain above 90 percent. Barata (2019) used a simple convolutional neural network to identify whether a cough event had occurred. The steps of the algorithm included extracting the audio event, preprocessing, and cough detection, which simplifies into two categories: cough or non-cough. ...
Article
Full-text available
The COVID-19 virus has irrevocably changed the world since 2020, and its incredible infectivity and severity have sent a majority of countries into lockdown. The virus’s incubation period can reach up to 14 days, enabling asymptomatic hosts to transmit the virus to many others in that period without realizing it, thus making containment difficult. Without actively getting tested each day, which is logistically improbable, it would be very difficult for one to know if they had the virus during the incubation period. The objective of this paper’s systematic review is to compile the different tools used to identify coughs and ascertain how artificial intelligence may be used to discriminate a cough from another type of cough. A systematic search was performed on Google Scholar, PubMed, and MIT library search engines to identify papers relevant to cough detection, discrimination, and epidemiology. A total of 204 papers have been compiled and reviewed and two datasets have been discussed. Cough recording datasets such as the ESC-50 and the FSDKaggle 2018 and 2019 datasets can be used for neural networking and identifying coughs. For cough discrimination techniques, neural networks such as k-NN, Feed Forward Neural Network, and Random Forests are used, as well as Support Vector Machine and naive Bayesian classifiers. Some methods propose hybrids. While there are many proposed ideas for cough discrimination, the method best suited for detecting COVID-19 coughs within this urgent time frame is not known. The main contribution of this review is to compile information on what has been researched on machine learning algorithms and its effectiveness in diagnosing COVID-19, as well as highlight the areas of debate and future areas for research. This review will aid future researchers in taking the best course of action for building a machine learning algorithm to discriminate COVID-19 related coughs with great accuracy and accessibility.
... In recent years, a variety of acoustic features have been evaluated and compared for use in cough detection (Miranda et al., 2019;Pramono et al., 2019). As deep neural network (DNN) has developed and shown excellent performance in fields such as vision, it became possible to detect cough sounds with high accuracy by applying DNN to acoustic features (Barata et al., 2019(Barata et al., , 2020Hossain et al., 2020). Recently, Ahamad et al. (2020) proposed a supervised model to identify early stage symptoms predicting COVID-19 disease, which revealed that fever (41.1%) and cough (30.3%) are the most frequent and significant predictive symptoms of COVID-19. ...
Article
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 of the cough sound, conventional features such as spectrograms, mel-scaled spectrograms, and mel-frequency cepstral coefficients (MFCC) were reinforced by utilizing their velocity (V) and acceleration (A) maps in this work. VGGNet, GoogLeNet, and ResNet were simplified to binary classifiers, and were named V-net, G-net, and R-net, respectively. To find the best combination of features and networks, training was performed for a total of 39 cases and the performance was confirmed using the test F1 score. Finally, a test F1 score of 91.9% (test accuracy of 97.2%) was achieved from G-net with the MFCC-V-A feature (named Spectroflow), an acoustic feature effective for use in cough detection. The trained cough detection model was integrated with a sound camera (i.e., one that visualizes sound sources using a beamforming microphone array). In a pilot test, the cough detection camera detected coughing sounds with an F1 score of 90.0% (accuracy of 96.0%), and the cough location in the camera image was tracked in real time.
... [1][2][3] Sound has been used for respiratory disease diagnostics for centuries, 4 but recent advances in acoustic modeling and machine learning have compounded what we can learn by listening to patients cough, sneeze, wheeze, breathe, speak, and snore. [5][6][7][8][9] Those behaviors all have information-rich acoustic signatures that can be used to screen for the probability of infection or pathology (e.g. TB, 10 COVID-19, [11][12][13][14][15] and speech pathology 3,[16][17][18]. ...
Article
Full-text available
Objective Respiratory illnesses have information-rich acoustic biomarkers, such as cough, that can potentially play an important role in screening populations for disease risk. To realize that potential, datasets of paired acoustic-clinical samples are needed for the development and validation of acoustic screening models, and protocols for collecting acoustic samples must be efficient and safe. We collected cough acoustic signatures at a high-throughput SARS-CoV-2 testing site on a college campus. Here, we share logistical details and the dataset of acoustic cough signatures paired with the gold standard in SARS-CoV-2 testing of SARS-CoV-2 genomic sequences using qRT-PCR. Methods Cough recordings were collected in winter-spring 2021 at a rural residential college (Sewanee, TN, USA), where approximately 2000 students were tested for SARS-CoV-2 on a weekly basis. Cough collection was managed by student volunteers using custom software. Results 4302 coughs were recorded from 960 participants over 11 weeks. All coughs were COVID-19 negative. Approximately 30 s were required to check-in a participant and collect their cough. Conclusion The value of acoustic screening tools depends upon our ability to develop and implement them reliably and quickly. For that to happen, high-quality datasets and logistical insights must be collected and shared on an ongoing basis.
Article
Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI- powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.
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Introduction: Nocturnal cough is a burdensome asthma symptom. However, knowledge about the prevalence of nocturnal cough in asthma is limited. Furthermore, prior research has shown that nocturnal cough and impaired sleep quality are associated with asthma control, but the association between these two symptoms remains unclear. This study further investigates the potential of these symptoms as markers for asthma control and the accuracy of automated, smartphone-based passive monitoring for nocturnal cough detection and sleep quality assessment. Methods and analysis: The study is a multicentre, longitudinal observational study with two stages. Sensor and questionnaire data of 94 individuals with asthma will be recorded for 28 nights by means of a smartphone. On the first and the last study day, a participant’s asthma will be clinically assessed, including spirometry and fractionated exhaled nitric oxide levels. Asthma control will be assessed by the Asthma Control Test and sleep quality by means of the Pittsburgh Sleep Quality Index. In addition, nocturnal coughs from smartphone microphone recordings will be labelled and counted by human annotators. Relatively unrestrictive eligibility criteria for study participation are set to support external validity of study results. Analysis of the first stage is concerned with the prevalence and trends of nocturnal cough and the accuracies of smartphone-based automated detection of nocturnal cough and sleep quality. In the second stage, patient-reported asthma control will be predicted in a mixed effects regression model with nocturnal cough frequencies and sleep quality of past nights as the main predictors. Ethics and dissemination: The study was reviewed and approved by the ethics commission responsible for research involving humans in eastern Switzerland (BASEC ID: 2017–01872). All study data will be anonymised on study termination. Results will be published in medical and technical peer-reviewed journals.
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Wireless distributed systems as used in sensor networks, Internet-of-Things and cyber-physical systems, impose high requirements on resource efficiency. Advanced preprocessing and classification of data at the network edge can help to decrease the communication demand and to reduce the amount of data to be processed centrally. In the area of distributed acoustic sensing, the combination of algorithms with a high classification rate and resource-constraint embedded systems is essential. Unfortunately, algorithms for acoustic event detection have a high memory and computational demand and are not suited for execution at the network edge. This paper addresses these aspects by applying structural optimizations to a convolutional neural network for audio event detection to reduce the memory requirement by a factor of more than 500 and the computational effort by a factor of 2.1 while performing 9.2% better.
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Telehealth has shown potential to improve access to healthcare cost-effectively in respiratory illness. However, it has failed to live up to expectation, in part because of poor objective measures of symptoms such as cough events, which could lead to early diagnosis or prevention. Considering the burden that these conditions constitute for national health systems, an effort is needed to foster telehealth potential by developing low-cost technology for efficient monitoring and analysis of cough events. This paper proposes the use of local Hu moments as a robust feature set for automatic cough detection in smartphone-acquired audio signals. The final system feeds a k -nearest-neighbor classifier with the extracted features. To properly evaluate the system in a diversity of noisy backgrounds, we contaminated real cough audio data with a variety of sounds including noise from both indoor and outdoor environments and noncough events (sneeze, laugh, speech, etc.). The created database allows flexible settings of signal-to-noise ratio levels between background sounds and events (cough and noncough). This evaluation was complemented using real patient data from an outpatient clinic. The system is able to detect cough events with high sensitivity (up to 88.51%) and specificity (up to 99.77%) in a variety of noisy environments, overcoming other state-of-the-art audio features. Our proposal paves the way for ubiquitous cough monitoring with minimal disruption in daily activities.
Technical Report
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.
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