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.

Author: Pauwels, Johan
Affiliation: Queen Mary University of London
AES Conference: AES 2023 International Conference on Spatial and Immersive Audio (August 2023)
Paper Number: 23
Publication Date: August 23, 2023
Subject: Paper

Download Now (479 KB)

This paper is Open Access which means you can download it for free.

No AES members have commented on this paper yet.

Subscribe to this discussion

To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:
Username:
Password:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.