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

Journal: The Journal of the Acoustical Society of America
Published Date:

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.

Authors

  • Kyla McMullen
    Department of Computer and Information Science and Engineering, University of Florida, 432 Newell Drive, Gainesville, Florida 32611, USA.
  • Yunhao Wan
    Department of Computer and Information Science and Engineering, University of Florida, 432 Newell Drive, Gainesville, Florida 32611, USA.