HRTF Performance Evaluation: Methodology and Metrics for Localisation Accuracy and Learning Assessment
David Poirier-Quinot, Martin S. Lawless, Peter Stitt, Brian F. G. Katz
Abstract
Through a review of the current literature, this chapter defines a methodology for the analysis of HRTF localisation performance, as applied to assess the quality of an HRTF selection or learning program. A case study is subsequently proposed, applying this methodology to a cross-comparison on the results of five contemporary experiments on HRTF learning. The objective is to propose a set of steps and metrics to allow for a systematic assessment of participant performance (baseline, learning rates, foreseeable performance plateau limits, etc.) to ease future inter-study comparisons.
Topics & Concepts
Baseline (sea)Computer scienceSelection (genetic algorithm)Set (abstract data type)Artificial intelligenceMachine learningQuality (philosophy)Programming languageEpistemologyPhilosophyOceanographyGeologyAdvanced Research in Systems and Signal Processing