J. agric. Engng Res. (2001) 79 (4), 449}457
doi:10.1006/jaer.2001.0719, available online at http://www.idealibrary.com on
AP*Animal Production Technology
Recognition System for Pig Cough based on Probabilistic Neural Networks
A. Chedad; D. Moshou; J. M. Aerts; A. Van Hirtum; H. Ramon; D. Berckmans
Laboratory for Agricultural Buildings Research, K.U. Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium; e-mail of corresponding author:
daniel.berckmans@agr.kuleuven.ac.be
Laboratory for Agro-Machinery and Processing, K.U. Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium; e-mail:
dimitrios.moshou@agr.kuleuven.ac.be
(Received 12 June 2000; accepted in revised form 6 March 2001; published online 18 May 2001)
Until now the use of acoustic bio-responses in bio-environment control as indicators of animal-condition is
limited to human perception. Coughing is a frequent symptom of many respiratory diseases a!ecting the
airways and lungs of humans and animals.
Registration of coughs from di!erent pigs in a controlled test chamber was done in order to analyse the
acoustical signal. A new approach is presented to distinguish cough sounds from other sounds, such as grunts,
metal clanging and background noise, using neural networks as the classi"cation method. Other signals (such as
grunts, metal clanging, etc.) could also be detected.
The best performance was obtained with a hybrid classi"er that classi"es coughs and metal clanging
separately from the rest, giving better results compared to a probabilistic neural network (PNN) alone. The
hybrid classi"er, which consists of a 2- and a 4-class PNN, gave high discrimination performance in the case of
grunts, metal clanging and background noise (91)4, 63)9 and 82)6%, respectively) and a performance of (91)9%)
for correct classi"cation in the case of coughs.
2001 Silsoe Research Institute
1. Introduction
Coughing is one of the body's defence mechanisms,
against the entry of materials into the respiratory system.
Clinically, coughing is one of the symptoms most frequently present in many diseases a!ecting the airways
and lungs, and is an early symptom of some diseases,
such as asthma, bronchitis and chronic bronchitis (Hirschberg & Szende, 1982). It involves a natural bio-signal
indicating a possible health problem. It is well known
that coughing is associated with a sudden expulsion of
air, which is accompanied by a typical sound. Changes in
the character of the sound of the cough might re#ect
pathological conditions in the lungs. The objective registration of cough sound as a diagnostic tool is still absent
from current medical and veterinary practice in comparison with the registration of the electro-encephalogram
(EEG), electro-myogram (EMG), electro-cardiogram
(ECG), etc.
0021-8634/01/080449#09 $35.00/0
The cough sounds are generally classi"ed according
to empirical observation. Two possibilities exist for
cough sound registration and analysis. One is the registration of the acoustic pressure accompanying in the
form of time}amplitude, the tussiphonogram, as in
Korpas et al. (1987). The other approach uses frequency
analysis, as in Subburaj et al. (1996) and Korpas et al.
(1996). The sounds associated with coughing can be
registered as a tussiphonogram and analysed with
respect to shape, duration, intensity, frequency
components, etc.
Coughing can indicate to a producer that illness may
be present in a pig house. The occurrence of coughing
among piglets is shown to depend on air velocity and
temperature during transport and housing conditions
(Geers et al., 1986). E!ects of environmental variables
such as aerial ammonia and dust concentration on the
respiratory tract are observed (Urbain et al., 1996). For
this reason the amount of coughing and the frequency of
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2001 Silsoe Research Institute
450
A. C H ED AD E¹ A ¸.
occurrence is widely recognized as a natural alarm by
experienced herdsmen. Close human attention is not so
evident today due to the great number of animals in
a potentially harmful environment (Attwood et al., 1986).
So, in general, health control and consequently welfare
would bene"t from continuous automated, contactless
and on-line cough observation in the pig house.
Some contactless cough recognizers are presented in
literature on pigs. For the early detection of coughs in pig
houses, a relatively cheap system is required. Robertson
and Benzie (1989) built a system to detect coughs in pig
houses based on a "xed template shape of the amplitude
of a cough signal in the time domain, after the signal was
split into eight frequency channels. The main problem of
this technique was the inability of the system to discriminate between coughs and other sounds which produced
an audio signal of similar pro"le. Speech processing was
performed on some pig vocalizations, including 15 records of coughing of di!erent slaughter pigs, for the
detection of stress (Van Compernolle et al., 1992).
Analysing the number of formants of the spectrogram
within the linear prediction coe$cients (LPC) spectrum
results in a 87%$2)8 recognition of pig coughing.
The objective of this paper is to describe an algorithm
which enables the discrimination of cough sounds from
other background noise, such as metal clanging, grunting
and other background noise, using advanced classi"cation methods, such as neural networks. The presented
algorithm can be implemented in a system for on-line pig
cough recognition. The features that are used for classi"cation are the components of the power spectral density
(PSD) of the di!erent sounds. The neural architecture
that is presented in this work, probabilistic neural networks is used for the classi"cation of the sounds.
2. Materials and methods
2.1. ¹est installation
The laboratory installation used, described in Urbain
et al. (1993), consists of a metal construction of dimension
2 m long by 0)8 m wide by 0)95 m high covered with
transparent plastic in order to control the microenvironment around the animal. A schematic representation that
clari"es the interconnections of the measuring apparatus
is shown in Fig. 1.
As environmental variables may act upon the respiratory tract and so upon the cough signal, it was necessary
to control NH concentration, dust concentration, tem
perature and air#ow rate in the closed test installation.
Experiments were performed at 213C with an NH
concentration less than 1 p.p.m., dust particles of size
greater than 1 lm "ltered out, and an air#ow rate of
10 m h\. Pigs (Landrace) used in the study were 14}15
weeks old. One of the pigs was a male with a weight of
Fig. 1. Schematic representation of the test procedure for recording the sounds; the unidirectional microphone is connected to
a standard 16 bit sound card supplemented by appropriate software
RE C O G NI T IO N S YS T EM F OR P I G CO U G H
approximately 45 kg. The three others were females
weighing 40}50 kg. The coughs were chemically induced
by an irritating substance (citric acid vapour) produced
by an evaporator. A quantity of 50 ml was evaporated
during 15 min. The entire procedure is explained in detail
in Moreaux et al. (1999).
The system used a Pentium II, 200 MHz personal
computer, with a standard 16 bit sound card supplemented by appropriate software and an inexpensive unidirectional condenser microphone (Model 16 A,
SHURE) with a frequency response of 20}20,000 Hz. The
signal-to-noise ratio of the used microphone was 48 at
74 dB sound pressure level (SPL). For reference, 74 dB
SPL was the level of a talker 2)5 cm away from the
microphone. The system noise was less than 0)01 V,
which was low in comparison with the level of any sound
signal produced in the test installation. For these reasons
the accuracy of the used microphone system was considered as adequate for the sound recording. The microphone was attached and positioned in the cage through
an aperture into the plastic cover, placed 0)4}1 m from
the animal depending on the position of the animal in the
cage. The sample rate chosen was equal to 22,050 Hz,
because the frequencies of a typical cough are below
10,000 Hz. The measured signal of a typical pig cough
and its PSD is shown in Fig. 2. The central frequency of
the PSD is around 1610 kHz. The recordings were done
by using the commercially available &Cool edit pro' software (Syntrillium Software Corporation) and captured
data were saved in windows wave sound format. The
data were stored on a compact disc. Each experiment
induced coughing on one individual animal. A whole
gamut of sounds was received by recording the full
30 min of each experiment and these sounds can be
distinguished in di!erent groups. Firstly, the sounds
produced by the laboratory animal itself, besides the
coughing sounds, consist mostly of grunting sounds and
sounds due to respiration. A second group of sounds is
due to the procedure followed for cough induction. On
one hand, the interaction of the laboratory animal with
the test installation causes noises, typically for the installation material used. In this case, the test installation is of
metallic construction, so animal movements may cause
metal clanging. On the other hand, the procedure itself
includes some noise e.g. the ventilation inside the test
installation needed for evaporation of the irritating substance. Finally, there are background noises mainly due
to ventilation in the chamber containing the test installation and the presence of researchers (e.g. talking, shutting
of doors, etc).
The duration of each measurement was about 30 min
consuming approximately 80 MB of disc space. During
the 30 min of measurements, 57, 49, and 52 coughs and 5,
6 and 5 grunts could be collected, respectively, from the
451
three gilts. Fifty-four coughs and 7 grunts were collected
from the barrow. Over the four experiments, 50 metal
clanging and 69 background noise sound samples could
be collected.
2.2. Collecting samples
In order to build a system that is capable of distinguishing coughs from other sounds, a high number
of samples had to be collected. For this purpose, a total of
354 sounds were collected, comprising coughs from
four di!erent individual pigs (212 samples), metal clanging (50 samples), grunts (23 samples), and background
noise (69 samples). Each of the individual sounds
is auditively recognized by a human expert as a cough
or not.
The samples were split into two groups after auditive
recognizing; the training set (23 sounds from each class)
and the testing set (23 sounds from each class, di!erent
than the sounds used for training with the exception of
coughs, where 115 coughs were used for testing). Then
the power spectra density (PSD) of the sounds was calculated using a fast Fourier transform (FFT) of 128
points. Since the FFT is symmetric, the PSD had 64
components.
Speci"c algorithms for calculating PSDs are widely
available in many mathematical software for physicists
and engineers. For example, the commonly used MATLAB system, contains a toolbox (Matlab, 1998) for signal
processing with numerous sound processing routines.
A representative spectrum of a cough is presented in
Fig. 2. In this way, the problem of classifying sounds was
reduced to the problem of classifying vectors with the
same number of components i.e. PSD vectors with 64
components. Those vectors are further used for training
and testing the neural network.
In total, 115 coughs have been used for testing, because
of the importance of the correct recognition of cough
sounds. The testing set was used for the "nal evaluation
of the classi"er. Some representative examples of the
spectra of sounds belonging to each class are presented in
Fig. 3. The cough sounds from the four di!erent pigs had
a similar frequency content (probability p'0)05 by Student's t test for paired comparisons applied to the mean
spectra of 120 coughs and more speci"cally 30 coughs
from each of the four pigs over the entire frequency
range).
2.3. Acoustical signals
For the obvious reason of non-"xed animal position
with respect to the microphone, the exact amplitude
452
A. C H ED AD E¹ A ¸.
Fig. 2. The time signal of a pig cough and its power spectrum density (PSD)
value is not suitable for sound characterization. The
duration of grunts compared to coughing are generally
longer. The average duration of the 212 cough samples
is 0)5$0)09 seconds and the average duration of the
23 grunt samples is 1)2$0)15 s. The noise duration
was not taken into account because it shows a great
variability.
2.4. Probabilistic neural networks
A well-known decision rule used to classify patterns
uses the minimization of the &expected risk' of misclassi"cation. Such a strategy is called the &Bayes Strategy' and
can be applied to problems containing a number of
categories. Let us consider a two-category situation in
RE C O G NI T IO N S YS T EM F OR P I G CO U G H
453
Fig. 3. The power spectrum density (PSD) of: (a) a pig cough; ( b) metal clanging; (c) grunting and (d) background noise
which the state of h is known to be either h or h . Then,
the Bayes decision rule g becomes:
g(X)"h if h l f (X)*h l f (X)
(1)
g(X)"h if h l f (X))h l f (X)
(2)
where g(X) is the decision on test vector X, f (X) and
f (X) are probability density functions (PDF) for categories A and B; l represents the loss incurred by misclas
sifying a vector of class A to class B; and l represents the
loss incurred by misclassifying a vector of class B to A. In
the case that the decision is correct, the losses l or l are
taken to be equal to zero. The a priori probability of
occurrence of a pattern from category A is given by h ;
and h de"ned by h "1!h is the a priori probability
that h belongs to class h . From the above equations, it
follows that when the vector X belongs to the boundary
of the classes A and B, the following equation can be
written:
f (X)"Kf (X)
where the coe$cient K is given by
(3)
K"h l /h l
(4)
Bayesian classi"cation requires a PDF for each class.
It is often di$cult to determine the PDF with a high
accuracy. The probabilistic neural network (PNN) is
based upon work done in the 1960s (Specht, 1966). It has
been used successfully (Specht, 1990) to solve a diverse
group of classi"cation problems. The PNN has been
applied successfully to a wide range of applications, including the airport &bomb sni!er' of Shea and Lin (1989)
that detects explosives in luggage, the vector-cardiogram
interpretation (Specht, 1967) and, more recently, the
hull-to-emitter correlation on radar hits, as in Maloney
and Specht (1989).
Fortunately, Parzen (1962) showed how a family of
PDFs functions can be expressed as a product of univariate kernels:
1
1
f (X)"
(2n). p. n
n
; exp [!(X!X )2 (X!X )/2p] (5)
G
G
G
where i is the pattern number; n is the total number of
training patterns in class A; X is the ith training pattern
G
from category p ; p is the spread or standard deviation;
P is the dimensionality of measurement space; superscript T is the transpose of a vector; and X is the test
vector to be classi"ed.
The formula may appear complicated but the idea
is as follows. Add up the values of the n-dimensional
454
A. C H ED AD E¹ A ¸.
weighting C :
I
hB nA l
C "! I I
I
hA nB lA
I
I
(8)
I
where nA is the number of training patterns from catI
egory A ; and nB is the number of the training patterns
I
I
from category B .
I
Note that C is the ratio of a priori probabilities,
I
multiplied by the ratio of losses and multiplied by the
ratio of samples in each class. This "nal ratio cannot be
determined from the statistic of the training samples, but
only from the signi"cance of the decision. If there is no
particular reason for biasing the decision, C may simI
plify to !1 (an inverter).
Fig. 4. Probabilistic neural network (PNN); X1}Xn , input; fA1 ( X)
and fB1 ( X) are probability density functions for categories A and
B; O1}On , output
3. Results and discussion
3.1. Discrimination of coughs from metal clanging
Gaussians, evaluated at the training vector point in an
n-dimensional space, and scale the sum to produce the
estimated probability density at that point. However, the
sum is not limited to being Gaussian. It can, in fact,
approximate any smooth density function.
Figure 4 shows a neural network organization for the
classi"cation of input vector X into two categories. Each
part of the input unit is connected to all the pattern units.
The set of weightings W "(=R , =R ,2, =R ) asso
.
0
ciated with a given pattern neurone represents a training
vector. Next, the pattern neurone computes the product
XW by subtracting 1 and dividing by p, and applying
0
the non-linear function that results "nally in the term
exp [(W X!1)/p]. Assuming that both X and W are
0
0
normalized to unity, this is equivalent to using:
exp [!(W !X)2 (W !X)/2p]
(6)
0
0
The network is trained by setting the weighting vector
W in one of the pattern units equal to each of the
0
patterns in the training set X and then connecting the
0
output of the pattern units to the appropriate summation
unit.
Each neurone in the summation units sums the inputs
from the pattern units associated with a given class. The
output of each summation layer neurone becomes
nA
fA (X)" exp [(XR X!1)/p]
(7)
G
G
In the output, or decision units, each neurone forms
a comparison, outputting one if fA (X) is greater than
I
C fB (X), and otherwise zero. They have only a variable
I I
In the case of the classi"cation of cough sounds and
particularly the discrimination of cough sounds from
other sounds, the problem of separating the cough
sounds from the metal clanging is most di$cult because
these sounds have a similar frequency content (p'0)05
over the 0}6)5 kHz frequency range and p(0)05 over the
6)5}10 kHz frequency range by Student's t test for paired
comparisons applied to the mean spectra of 48 coughs
from 12 coughs of four di!erent pigs and 48 metal clanging). Before training the PNN, the spectra had to be
normalized by dividing them by their Euclidean norm.
The normalization step is necessary because the spectral
power levels exhibit a large variation even inside the
same class. This variation is due to the distance
and direction between the pigs and the microphone.
Due to low system noise (less than 0)01 V as mentioned
earlier) the variation in distance and position of the
pigs did not a!ect the quality of the recording.
For varying spread between 0)001 and 1 in steps of
0)001 the correct recognition percentages are given in
Fig. 5.
A separate analysis of sounds (15 from each class) was
used for testing the classi"cation performance of the
PNN. For spreads less than 0)006, the PNN classi"es the
cough sounds perfectly (100%) but many metal clanging
sounds are misclassi"ed as coughs. For a spread greater
than 0)015, the correct recognition percentages converge
to the same value of 94%. For this classi"er the spectrum
had 64 components.
The PNN shows superior performance in the discrimination of cough from metal clanging (100%) while the
other classi"ers achieve around 94% for both classes
with the minimum distance classi"er achieving 89% on
455
RE C O G NI T IO N S YS T EM F OR P I G CO U G H
Fig. 5. Correct recognition percentages of a probabilistic neural network using diwerent spreads:
coughs and 94% on metal clanging. The performance of
the PNN is better because the boundaries between the
di!erent classes are determined by a PDF, which is not
the case in a minimum distance classi"er.
3.2. Multiple class classi,er
For the case of discrimination between coughs,
metal clanging, grunting and background noise, other
classi"ers had to be developed. Only probabilistic neural
networks were trained to classify the four di!erent types
of sounds.
For the PNN, the correct classi"cation percentages
for di!erent spreads are shown in Fig. 6. It is clear
that the 4-class PNN using di!erent spreads gives
high discrimination performance (90}100%) in the
case of grunts and noise but the performance in the
case of coughs and metal clanging is low (60}80%).
The confusion matrix describes how many of the
sounds whose correct class appears in the "rst
column have been misclassi"ed as belonging to another
class.
The confusion matrix for a 4-class PNN classi"er that
has a spread equal to 1 is given in Table 1. It is evident
that neither method can give satisfactory results in the
case of coughs and metal clanging, while the results
are much better for the grunting and noise case classi"cation. This result leads to the consideration of a hybrid
scheme where a combination of the 2-class PNN based
, coughs;
, metal clanging
classi"er with the 4-class PNN based classi"er would
take place.
The hybrid classi"er switches from the 4-class
PNN classi"er to the 2-class PNN classi"er when
a sound is classi"ed either as a cough or metal clanging from the 4-class classi"er. Only then the 2-class
PNN estimates the class of the sound anew, otherwise it remains inactive. This way, a better class prediction is produced since for discriminating between
coughs and metal clanging the 2-class PNN is
superior compared to the 4-class PNN while the
4-class PNN separates the other sounds with high
performance.
The confusion matrix for such a scheme is shown in
Table 2. The number of sample sounds used for testing
was 23 for each class category. However, in the case of
cough sounds the number of tested examples was 115, so
the results for the coughs are presented as an average
value over "ve di!erent groups of coughs containing 23
cough samples each. The rest of the coughs (74) were not
used for validating because the results obtained using the
115 cough samples were considered representative
enough for testing the performance of the classi"er. The
results presented in confusion Table 2 show that the
hybrid classi"er performs much better than the 4-class
PNN classi"er alone.
The positive cough-recognition of 92)2% obtained
with the hybrid classi"er is slightly better than the best
accuracy found in literature (87% in Van Compernolle
et al., 1992).
456
A. C H ED AD E¹ A ¸.
Fig. 6. The correct recognition percentages for the 4-class classixer for a spread varying between 0 and 1:
grunts;
, metal clanging;
, background noise
Future extension of the proposed method to recognize
natural coughs is being planned. A similar extension was
presented in Korpas et al. (1996) for human cough sound.
According to their results there was no signi"cant di!erence for sound pattern, intensity and duration, but spectrographically chemically induced coughs show a
decreased intensity of frequencies above 2000 Hz compared to normal voluntary coughs.
4. Conclusions
A new approach is presented for the continuous
detection and classi"cation of coughs in pig houses. The
, coughs;
,
accurate detection of coughs is a matter of great economic importance since it can reveal the possibility of an
epidemic. If the epidemic is handled early enough, it can
be treated e!ectively.
The tools for detecting coughs were a standard cheap
microphone and a PC equipped with a sound card.
Advanced signal processing methods were used for the
detection of the sounds from raw recorded signals. For
the classi"cation, novel neural network based techniques
were used based on probabilistic neural networks. The
resulting system for detection and classi"cation of coughs
has a rather high performance of 91)9% of correct classi"cation. Future extension of the system to recognize
di!erent types of coughs that are related to certain
Table 1
Confusion matrix for the 4-class PNN (actual numbers); there are 25 false negative coughs being misclassi5ed into the other
categories while there is one false positive cough which is actually a metal clanging sound
Actual sound
Actual
Actual
Actual
Actual
coughs
metal clanging
grunting
background noise
Predicted
coughs
Predicted
metal
clanging
Predicted
grunting
Predicted
background
noise
80
1
0
0
15
14
1
1
10
2
21
3
10
6
1
19
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RE C O G NI T IO N S YS T EM F OR P I G CO U G H
Table 2
Confusion matrix for the 4-class hybrid classi5er especially constructed for the current problem (actual numbers); there are 9 false
negative coughs being misclassi5ed into the other categories while there are no false positive coughs; false positive errors have been
decreased with respect to the results of the PNN classi5er presented in Table 1
Actual sound
Actual
Actual
Actual
Actual
coughs
metal clanging
grunting
background noise
Predicted
coughs
Predicted
metal
clanging
Predicted
grunting
Predicted
background
noise
106
0
0
0
4
15
1
1
2
2
21
3
3
6
1
19
diseases is being planned. This step is important since the
origin of certain coughs could be due to other environmental reasons, such as dust and humidity and not due to
the presence of a certain viral infection.
Acknowledgements
The authors are grateful to the Ministry of Small Enterprises and Agriculture (Belgium) for "nancial support.
Special thanks are also due to Prof. W. Lauriks of the
Laboratory of Acoustics and Thermal Physics (Leuven)
for good advice. I would like to express my appreciation
to my colleagues D. Moshou and J. Hendriks for their
invaluable help.
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