Applied methods for sparse sampling of head-related transfer functions

Lior Arbel, Zamir Ben-Hur, David Lou Alon, Boaz Rafaely

Research output: Contribution to journalConference articlepeer-review


Production of high fidelity spatial audio applications requires individual head-related transfer functions (HRTFs). As the acquisition of HRTF is an elaborate process, interest lies in interpolating full length HRTF from sparse samples. Ear-alignment is a recently developed pre-processing technique, shown to reduce an HRTF's spherical harmonics order, thus permitting sparse sampling over fewer directions. This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. These methods consist of generating unique vector sets for HRTF representation. The methods were tested over an HRTF dataset, indicating that interpolation errors using small sampling schemes may be further reduced by up to 5 dB in comparison with spherical harmonics interpolation.

Original languageEnglish
Pages (from-to)446-450
Number of pages5
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 1 Jan 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021


  • Head-related transfer functions (HRTFs)
  • Orthogonal matching pursuit
  • Principal component analysis
  • Spatial audio
  • Spherical-harmonics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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