Estimation of pinna notch frequency from anthropometry: An improved linear model based on principal component analysis and feature selection

In this paper, anthropometric data from a database of Head-Related Transfer Functions (HRTFs) is used to estimate the frequency of the first pinna notch in the frontal part of the median plane. Given the presence of high correlations between some of the anthropometric features, as well as repeated values for the same subject observations, we propose the introduction of Principal Component Analysis (PCA) to project the features onto a space where they are more separated. We then construct a regression model employing forward step-wise feature selection to choose the principal components most capable of predicting notch frequencies. Our results show that by using a linear regression model with as few as three principal components, we can predict notch frequencies with a cross-validation mean absolute error of just about 600 Hz.