Hybrid neural network design and implementation on FPGA for infant cry recognition

It has been found that the infant's crying has much information on its sound wave For small infants crying is a form of communication, a very limited one, but similar to the way adults communicate In this work we present the design of an Automatic Infant Cry Recognizer hybrid system, that classifies different kinds of cries, with the objective of identifying some pathologies in recently born babies The system is based on the implementation of a Fuzzy Relational Neural Network (FRNN) model on a standard reconfigurable hardware like Field Programmable Gate Arrays (FPGAs) To perform the experiments, a set of crying samples is divided in two parts; the first one is used for training and the other one for testing The input features are represented by fuzzy membership functions and the links between nodes, instead of regular weights, are represented by fuzzy relations The training adjusts the relational weight matrix, and once its values have been adapted, the matrix is fixed into the FPGA The goal of this research is to prove the performance of the FRNN in a development board; in this case we used the RC100 from Celoxica The implementation process, as well as some results is shown.