Cross-validation of acquisition methods for near-field Head-Related Transfer Functions with a high distance resolution

Due to the distance-dependent characteristics of near-field Head-Related Transfer Functions (HRTFs), their acquisition procedure is much more complex than that of far-field HRTFs, which is already rather tedious. Recently, an approach to efficiently measure near-field HRTFs with a high distance resolution using a continuously moving sound source and a Least Mean Square (LMS) adaptive filtering algorithm has been proposed. Using this approach, we obtained a dataset containing horizontal-plane HRTF data of a Neumann's KU 100 dummy head for source distances from 19cm to 119cm with a resolution of 1cm. Among the various choices for evaluation and validation, a direct comparison with another HRTF dataset is a straightforward solution. Since the distance resolution of all existing near-field HRTF databases is much lower than the one of interest, we numerically computed the HRTFs with the same spatial resolution using the laser-scanned surface geometry of the dummy head and the Finite Element Method (FEM). The aim of this work is to objectively evaluate the similarity and discrepancy between the two HRTF datasets in terms of spectral characteristics and perceptual effects. The causes of disparities between the datasets are discussed.