Prediction of Head Related Transfer Functions Using Machine Learning Approaches

Abstract

The generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric information on pinnae. Most of the studies focus their personal auditory optimization analysis on the study of amplitude versus frequency on HRTFs, mainly in the search for significant elevation cues of frequency maps. Therefore, knowing the HRTFs of each individual is of considerable help to improve sound quality. The following work proposes a methodology to model HRTFs according to the individual structure of pinnae using multilayer perceptron and linear regression techniques. It is proposed to generate several models that allow knowing HRTFs amplitude for each frequency based on the personal anthropometric data on pinnae, the azimuth angle, and the elevation of the sound source, thus predicting frequency magnitudes. Experiments show that the prediction of new personal HRTF generates low errors, thus this model can be applied to new heads with different pinnae characteristics with high confidence. Improving the results obtained with the standard KEMAR pinna, usually used in cases where there is a lack of information.