Identifikasi Arti Tangisan Bayi Versi Dunstan Baby Language Menggunakan Jarak Terpendek Dari Jarak Mahalanobis (Infant Cries Identification of Dunstan Baby Language Version using the Shortest Distance of Mahalanobis)

New born babies have the ability to express their basic needs through sounds. A system to understand the meaning of crying infants of aged 0-3 months is called Dunstan Baby Language (DBL), which was introduced in 2006. This research aimed to perform the modeling of codebook method with k-means clustering technique as feature matching, and Mel Frequency Cepstrum Coefficients (MFCC) as feature extraction to identify the infant cries. The infant cries identification of Dunstan Baby Language version used the shortest distance of mahalanobis. The treatment in this research was the combination of frame length: 25 ms, 40 ms and 60 ms, frame overlap of 0%, 40%, and 60%, and the number of codewords (number of clusters) of 1 to 29. The best accuracy in recognizing five types of crying Infants using mahalanobis distance can be achieved up to 83% when the frame length = 275, the overlap frame = 0.25, and the k = 17. Sound ‘heh’ was the most familiar, whereas sound ‘owh’ was always missunderstood and generally  known as ‘neh’ and ‘eairh’.Keywords: Codebook, Dunstan baby language, Mahalanobis Distance, MFC

Analyzing Infant Cries using a Committee of Neural Networks in Order to Detect Hypoxia Related Disorder

Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research is done to improve classi¯cation of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia- related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. These features are then fed into a neural network committee consisting of Radial Basis Function Neural Networks. Bootstrapping is used to generate the committee. This construction yields a multi-classifier system with an overall classi¯cation performance of 85% on the data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system. Keywords: Infant Cry Analysis; Feature reduction, Neural Networks; Support Vector Machine

Analyzing Infant Cries using a Committee of Neural Networks in Order to Detect Hypoxia Related Disorder

Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research is done to improve classi¯cation of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia- related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. These features are then fed into a neural network committee consisting of Radial Basis Function Neural Networks. Bootstrapping is used to generate the committee. This construction yields a multi-classifier system with an overall classi¯cation performance of 85% on the data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system. Keywords: Infant Cry Analysis; Feature reduction, Neural Networks; Support Vector Machine

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic