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Observing the Clustering Tendencies of Head Related Transfer Function Databases

This study offers a detailed description of the clustering tendencies of a large, standardized HRTF repository, and compares the quality of the results to those of a CIPIC database subset. The statistical analysis was implemented by applying k-means clustering on the log magnitude of HRTFs on the horizontal plane, for a varying number of clusters. A thorough report on the grouping behavior of the filters as the number of clusters increases revealed a superiority of the HRTF repository in describing common behaviors across equivalent azimuth positions, over the CIPIC subset, for the majority of the HRTF datasets.

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