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A machine learning tutorial for spatial auditory display using head-related transfer functions.

Authors :
McMullen, Kyla
Wan, Yunhao
Source :
Journal of the Acoustical Society of America; Feb2022, Vol. 151 Issue 2, p1277-1293, 17p
Publication Year :
2022

Abstract

This review presents a high-level overview of the uses of machine learning (ML) to address several challenges in spatial auditory display research, primarily using head-related transfer functions. This survey also reviews and compares several categories of ML techniques and their application to virtual auditory reality research. This work addresses the use of ML techniques such as dimensionality reduction, unsupervised learning, supervised learning, reinforcement learning, and deep learning algorithms. The paper concludes with a discussion of the usage of ML algorithms to address specific spatial auditory display research challenges. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00014966
Volume :
151
Issue :
2
Database :
Complementary Index
Journal :
Journal of the Acoustical Society of America
Publication Type :
Academic Journal
Accession number :
155493109
Full Text :
https://doi.org/10.1121/10.0007486