2016 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia
978-1-5090-4357-6/16/$31.00 ©2016 IEEE
Infant’s Cry Sound Classification using Mel-
Frequency Cepstrum Coefficients Feature Extraction
and Backpropagation Neural Network
Yesy Diah Rosita
Informatics Engineering Study Program Universitas Islam Majapahit
Mojokerto, Indonesia yesidiahrosita@gmail.com
Hartarto Junaedi
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
