A framework for designing head-related transfer function distance metrics that capture localization perception
| Autor: | W. Owen Brimijoin, Vamsi K. Ithapu, Ishwarya Ananthabhotla |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pulmonary and Respiratory Medicine
Sound localization Artificial neural network Computer science business.industry media_common.quotation_subject Pattern recognition Function (mathematics) Transfer function Head-related transfer function Benchmarking Computer Science::Sound Perception Pediatrics Perinatology and Child Health Metric (mathematics) Auditory Perception Humans Sound Localization Artificial intelligence business Algorithms Statistical hypothesis testing media_common |
| Zdroj: | JASA Express Letters. 1:044401 |
| ISSN: | 2691-1191 |
| DOI: | 10.1121/10.0003983 |
| Popis: | Linear comparisons can fail to describe perceptual differences between head-related transfer functions (HRTFs), reducing their utility for perceptual tests, HRTF selection methods, and prediction algorithms. This work introduces a machine learning framework for constructing a perceptual error metric that is aligned with performance in human sound localization. A neural network is first trained to predict measurement locations from a large database of HRTFs and then fine-tuned with perceptual data. It demonstrates robust model performance over a standard spectral difference error metric. A statistical test is employed to quantify the information gain from the perceptual observations as a function of space. |
| Databáze: | OpenAIRE |
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