Third Degree Volterra Kernel For Newborn Cry Estimation

Newborn cry analysis is a difficult task due to its nonstationary nature; combined to the presence of nonlinear behavior as well. Therefore, an adaptive hereditary optimization algorithm is implemented in order to avoid the use of windowing nor overlapping to capture the transient signal behavior. Identification of the linear part of this particular time series is carried out by employing an Autorregresive Moving Average (ARMA) structure; then, the resultant estimation error is approched by a Nonlinear Autorregresive Moving Average (NARMA) model, which realizes a Volterra cubic kernel by means of a bilinear homogeneous structure in order to capture burst behavior. Normal, deaf; asfixia; pain, and uncommon newborn cries are inspected for differentation.