Bringing truly immersive 3D audio experiences to the end user requires a fast and a user friendly method of predicting HRTFs. While machine learning based approaches for HRTF prediction hold potential, it can be challenging to determine the best work?ow for deployment given the iterative nature of data preprocessing, feature extraction, prediction, and performance evaluation. Here, we describe an automated, end to end pipeline for HRTF prediction and evaluation that simultaneously tracks data, code and model, allowing for a comparison of existing and new techniques against a single benchmark.
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