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

On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning