Abstract—This is a mathematical modelling study in computational science that recognized patterns used based on the biological data. The biological data were obtained through the number of clusters and the shape of each clustered means Interaural Level Differences (ILD) and were both analytically examined by the data classification study. Then the result of the classification study trained by the Artificial Neural Networks to build a master-template for a single array. Here, these typified single array patterns were exclusively tested for several curve fitted functions and the outcome was the probability density functions “pdf” with the linear regression parameters. This initial evaluation confirms two Gaussian functions were both suitable models for the data sets, and then coefficients of these functions verified for a correlation to be validated. In conclusion, a parameterized first-order Gaussian function can be used as the mathematical model for all ILD patterns; the energy efficiency is also discussed.

Index Terms—Energy efficiency, Gaussian function, machine learning, mathematical modelling.

The author is with Monash University, Melbourne, Victoria, Australia (email: uragun@hotmail.com).

Cite: Balemir Uragun, "The Mathematically Describable ILD Patterns," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 3, pp. 132-146, 2018.