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Engineering Brief

A Head-Related Transfer Function Database Consolidation Tool for High Variance Machine Learning Algorithms

Authors: Tsui, Benjamin; Kearney, Gavin

AES Convention 145 · Paper 451 · October 2018

Abstract

Binaural based machine learning applications generally require a large number of HRTF (Head-Related Transfer Function) measurements. However, building an HRTF database from measurements of a large number of participants can be a time-consuming and tedious process. An alternative method is to combine the data from different existing databases to create a large training dataset. This is a significant challenge due to the large difference in measurement angles, filter size, normalization schemes, and sample rates inherent in different databases. Consequently, training of some machine learning algorithms can be cumbersome, requiring significant trial and error with different data and settings. To facilitate convenient preparation of datasets, this paper presents a Matlab-based tool that allows researchers to prepare and consolidate various HRTF datasets across different databases in a robust and fast manner. The tool is available online: https://github.com/Benjamin-Tsui/HRTF_preprocessing

Details

Published in
AES Convention 145
AES Convention
145
Paper number
451
Publication date
October 6, 2018
Session subject
Posters: Spatial Audio
Affiliation
University of York, York, UK (See document for exact affiliation information.)
Type
Engineering Brief