Modeling of Head-Related Transfer Functions Through Parallel Adaptive Filters

Currently, sound spatialization techniques that utilize "individual" Head-Related Transfer Functions (HRTFs) require the intended listener to undergo lengthy measurements with specialized equipment. Alternatively, the use of generic HRTFs may contribute to additional localization errors. A third possibility that we are pursuing is the customization of HRTFs, performed on the basis of geometrical measurements of the intended listener to determine the appropriate parameters in a structural HRTF model. However, an initial step of decomposing measured HRTFs in order to reveal the parameters of the structural model must be performed. A new approach for the decomposition of HRTFs is suggested and evaluated on simulated examples. The potential of this method for the decomposition of measured HRTFs is discussed.