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.

Authors: Shahid, Faiyadh; Javeri, Nikhil; Jain, Kapil; Badhwar, Shruti
Affiliation: EmbodyVR Inc., San Mateo, CA, USA
AES Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality (August 2018)
Paper Number: P9-4
Publication Date: August 11, 2018 Import into BibTeX
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=19700

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