Publication Repository yessi
2016 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia
Infant’s Cry Sound Classification using MelFrequency Cepstrum Coefficients Feature Extraction
and Backpropagation Neural Network
Yesy Diah Rosita
Hartarto Junaedi
Informatics Engineering Study Program
Universitas Islam Majapahit
Mojokerto, Indonesia
yesidiahrosita@gmail.com
Informatics Engineering Department
Sekolah Tinggi Teknik Surabaya
Surabaya, Indonesia
hartarto.j@gmail.com, aikawa@stts.edu
Abstract— Crying is a communication method used by infants
given the limitations of language. Parents or nannies who have
never had the experience to take care of the baby will experience
anxiety when the infant is crying. Therefore, we need a way to
understand about infant’s cry and apply the formula. This
research develops a system to classify the infant’s cry sound using
MACF (Mel-Frequency Cepstrum Coefficients) feature
extraction and BNN (Backpropagation Neural Network) based
on voice type. It is classified into 3 classes: hungry, discomfort,
and tired. A voice input must be ascertained as infant’s cry sound
which using 3 features extraction (pitch with 2 approaches:
Modified Autocorrelation Function and Cepstrum Pitch
Determination, Energy, and Harmonic Ratio). The features
coefficients of MFCC are furthermore classified by
Backpropagation Neural Network. The experiment shows that
the system can classify the infant’s cry sound quite well, with 30
coefficients and 10 neurons in the hidden layer.
Keywords—infant’s cry sound; pitch; energy; harmonic ratio;
mel-frequency cepstrum coefficients; backpropagation neural
network
I. INTRODUCTION
There are many problems for parents or nannies because of
incomprehension infant’s language. So, we need a system that
is able to show the meaning of infant’s language. A
comprehension of infant’s language needs to reduce irritation,
anxiety, etc. On a panic situation, a parent or nanny will take
any action to calm down the infant even this is abusive action.
So, this research will discuss how to classify the infant’s cry
sound (based on voice type) and what solution is given to
overcome it.
Classification of infant’s cry sound is needed because
parents or nannies who don’t have any experiences, especially
young parents. They will feel uncomfortable when the infant is
crying. They don’t know what the infant wants. So, the infant
will be cried continuously.
On paper [12] an identification infant's cry using Matlab as
program language and Mel-Frequency Cepstrum Coefficients
(MFCC) algorithm has been tried to be done, which is the
identification of infant's cry successfully done as desired but
this research identified the voice that was certainly an infant's
cry, while in the real world sometimes a cat sound like the
sound of an infant's cry. Also with paper [1] does the same but
different with the previous study, this study classifies two kinds
of infant’s cry that is physiological status and medical disease.
Paper [4], it does identification of infant's “cry” and “no cry”
which more than 3 features is used. This research only
observes limits the values of features of infant's cry. Paper [8],
it does classification of infant’s cry into 3 kinds that consist of
normal, hypoacoustic and asphyxia. The research uses acoustic
characteristics extraction techniques like Linear Prediction
Coefficients (LPC) and MFCC as a feature with samples of 1
second, with 16 coefficients for every 50 ms/frame and
Adaptive Backpropagation Neural Network as a classifier. The
results obtained, of up to 98.67%.
Besides using MFCC as a feature to classify the infant's cry
sound based on voice type, we propose the development with
addition a multi features extraction in this research. There are 3
features (pitch with 2 approaches, energy, Harmonic Ratio).
So, the classification of infant's cry sound will be higher
accurate. With this research, it can be seen how accuracy of
infant's cry sound classification based on voice type that can be
helped to know the meaning of infant's cry sound and give a
solution.
The remainder of this paper organized as follow: first we
present methodology. Our design system on section 2. In
section 3, we present the experimental result and finally on
section 4 conclusion and the feature work of this paper.
II. METHODOLOGY
A methodology can be seen as the technique used to collect
and analyze data. The data collected have to be related to the
objective and problem statement. There are two types of
method that used in this study to obtain the relevant data: data
collection and interview.
A. Data Collection
We collect data of infant’s cry sound (0-3 months old) in 4
months. The average duration of recording infant’s cry sound is
5 seconds with file type .wav. The sound has been labeled by
their parents. In this case study, the number of data is 180 that
consist of 3 classes: hungry, discomfort, and tired. While the
number of training data is 150 and the number of testing data is
30. For negative data (not infant’s cry sound), we collect 25
978-1-5090-4357-6/16/$31.00 ©2016 IEEE
Infant’s Cry Sound Classification using MelFrequency Cepstrum Coefficients Feature Extraction
and Backpropagation Neural Network
Yesy Diah Rosita
Hartarto Junaedi
Informatics Engineering Study Program
Universitas Islam Majapahit
Mojokerto, Indonesia
yesidiahrosita@gmail.com
Informatics Engineering Department
Sekolah Tinggi Teknik Surabaya
Surabaya, Indonesia
hartarto.j@gmail.com, aikawa@stts.edu
Abstract— Crying is a communication method used by infants
given the limitations of language. Parents or nannies who have
never had the experience to take care of the baby will experience
anxiety when the infant is crying. Therefore, we need a way to
understand about infant’s cry and apply the formula. This
research develops a system to classify the infant’s cry sound using
MACF (Mel-Frequency Cepstrum Coefficients) feature
extraction and BNN (Backpropagation Neural Network) based
on voice type. It is classified into 3 classes: hungry, discomfort,
and tired. A voice input must be ascertained as infant’s cry sound
which using 3 features extraction (pitch with 2 approaches:
Modified Autocorrelation Function and Cepstrum Pitch
Determination, Energy, and Harmonic Ratio). The features
coefficients of MFCC are furthermore classified by
Backpropagation Neural Network. The experiment shows that
the system can classify the infant’s cry sound quite well, with 30
coefficients and 10 neurons in the hidden layer.
Keywords—infant’s cry sound; pitch; energy; harmonic ratio;
mel-frequency cepstrum coefficients; backpropagation neural
network
I. INTRODUCTION
There are many problems for parents or nannies because of
incomprehension infant’s language. So, we need a system that
is able to show the meaning of infant’s language. A
comprehension of infant’s language needs to reduce irritation,
anxiety, etc. On a panic situation, a parent or nanny will take
any action to calm down the infant even this is abusive action.
So, this research will discuss how to classify the infant’s cry
sound (based on voice type) and what solution is given to
overcome it.
Classification of infant’s cry sound is needed because
parents or nannies who don’t have any experiences, especially
young parents. They will feel uncomfortable when the infant is
crying. They don’t know what the infant wants. So, the infant
will be cried continuously.
On paper [12] an identification infant's cry using Matlab as
program language and Mel-Frequency Cepstrum Coefficients
(MFCC) algorithm has been tried to be done, which is the
identification of infant's cry successfully done as desired but
this research identified the voice that was certainly an infant's
cry, while in the real world sometimes a cat sound like the
sound of an infant's cry. Also with paper [1] does the same but
different with the previous study, this study classifies two kinds
of infant’s cry that is physiological status and medical disease.
Paper [4], it does identification of infant's “cry” and “no cry”
which more than 3 features is used. This research only
observes limits the values of features of infant's cry. Paper [8],
it does classification of infant’s cry into 3 kinds that consist of
normal, hypoacoustic and asphyxia. The research uses acoustic
characteristics extraction techniques like Linear Prediction
Coefficients (LPC) and MFCC as a feature with samples of 1
second, with 16 coefficients for every 50 ms/frame and
Adaptive Backpropagation Neural Network as a classifier. The
results obtained, of up to 98.67%.
Besides using MFCC as a feature to classify the infant's cry
sound based on voice type, we propose the development with
addition a multi features extraction in this research. There are 3
features (pitch with 2 approaches, energy, Harmonic Ratio).
So, the classification of infant's cry sound will be higher
accurate. With this research, it can be seen how accuracy of
infant's cry sound classification based on voice type that can be
helped to know the meaning of infant's cry sound and give a
solution.
The remainder of this paper organized as follow: first we
present methodology. Our design system on section 2. In
section 3, we present the experimental result and finally on
section 4 conclusion and the feature work of this paper.
II. METHODOLOGY
A methodology can be seen as the technique used to collect
and analyze data. The data collected have to be related to the
objective and problem statement. There are two types of
method that used in this study to obtain the relevant data: data
collection and interview.
A. Data Collection
We collect data of infant’s cry sound (0-3 months old) in 4
months. The average duration of recording infant’s cry sound is
5 seconds with file type .wav. The sound has been labeled by
their parents. In this case study, the number of data is 180 that
consist of 3 classes: hungry, discomfort, and tired. While the
number of training data is 150 and the number of testing data is
30. For negative data (not infant’s cry sound), we collect 25
978-1-5090-4357-6/16/$31.00 ©2016 IEEE