Estimation of Spectral Notches from Pinna Meshes: Insights from a Simple Computational Model

Simone Spagnol, Riccardo Miccini, Marius George Onofrei, Runar Unnthorsson, Stefania Serafin

Research output: Contribution to journalArticleScientificpeer-review

8 Downloads (Pure)

Abstract

While previous research on spatial sound perception investigated the physical mechanisms producing the most relevant elevation cues, how spectral notches are generated and related to the individual morphology of the human pinna is still a topic of debate. Correctly modeling these important elevation cues, and in particular the lowest frequency notches, is an essential step for individualizing Head-Related Transfer Functions (HRTFs). In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. We apply such a model to a highly controlled HRTF dataset built with the specific purpose of understanding the contribution of the pinna to the HRTF. Results show that the computational model is able to approximate the lowest frequency notch with improved accuracy with respect to other state-of-the-art methods. By contrast, the model fails to predict higher-order pinna notches correctly. The proposed approximation supplements understanding of the morphology involved in generating spectral notches in experimental HRTFs.

Original languageEnglish
Article number9507273
Pages (from-to)2683-2695
Number of pages13
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume29
DOIs
Publication statusPublished - 2021

Keywords

  • Acoustic measurements
  • audio signal processing
  • Computational modeling
  • head-related transfer functions (HRTFs)
  • HRTF individualization
  • Location awareness
  • pinna
  • Predictive models
  • Solid modeling
  • Spatial audio
  • spatial hearing
  • Speech processing
  • Three-dimensional displays

Fingerprint

Dive into the research topics of 'Estimation of Spectral Notches from Pinna Meshes: Insights from a Simple Computational Model'. Together they form a unique fingerprint.

Cite this