In this paper, the Hartufo Python toolkit is presented. Its aim is to provide an easy way to manage and preprocess HRTF data into a form that is suitable for use with all major machine learning tools. It consolidates typical boilerplate code into a single reusable library, in the hope that setting up experiments spanning multiple HRTF collections becomes easier, leading to novel insights. Additional benefits include increasing reproducibility and lowering the barrier to entry for machine learning and/or HRTF novices. Available as an open-source public beta, the majority of public HRTF collections are already supported, including auxiliary data such as photos and anthropometric measurements in addition to the auditory data. An overview of the library’s functionality is given in this text, ranging from practical examples for end-users to a discussion about the internal concepts of the library for those who want to extend it or interleave it with existing code.
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