  As spatial audio is enjoying a surge in popularity, data-driven machine
learning techniques that have been proven successful in other domains are
increasingly used to process head-related transfer function measurements.
However, these techniques require much data, whereas the existing datasets are
ranging from tens to the low hundreds of datapoints. It therefore becomes
attractive to combine multiple of these datasets, although they are measured
under different conditions. In this paper, we first establish the common ground
between a number of datasets, then we investigate potential pitfalls of mixing
datasets. We perform a simple experiment to test the relevance of the remaining
differences between datasets when applying machine learning techniques.
Finally, we pinpoint the most relevant differences.
