用统计方法验证听觉定位模型，提升人耳声音方向判断的准确性。
Statistical validation and full-sphere extension of a Bayesian model for human static sound localisation
- 基于贝叶斯框架，从感知特征和个体头相关传递函数推断声源方向。
- 在33名参与者数据上验证模型，能准确识别个体感知与频谱参数。
- 发现全向覆盖和高频保真度比插值算法更重要，适合听觉研究者参考。
听觉模型是研究空间听觉的核心工具，但其验证通常依赖启发式指标而非严谨的统计方法。本文提出两项贡献：首先，推导出显式似然函数，并通过模拟数据的参数恢复及33名被试的行为反应拟合，验证了该框架可可靠识别个体感官运动与频谱参数；其次，利用该框架比较四种头相关传递函数（HRTF）模板插值方法，结果表明全球空间覆盖与高频频谱保真度是模板质量的关键决定因素，而具体插值算法影响较小。这些成果表明，标准的模型化统计方法可同时解决空间听觉中的基础问题与实际应用如感知性HRTF评估。本文开源了Python实现。
原文摘要 · Abstract (English)
Auditory models are central tools for studying spatial hearing, yet their validation typically relies on heuristic performance metrics rather than principled statistical methods. We present two contributions building on a Bayesian sound localisation model that jointly infers sound direction from noisy perceptual features and individual head-related transfer functions (HRTFs). First, we derive an explicit likelihood function and validate it through parameter recovery on simulated data and fitting to behavioural responses from 33 participants, demonstrating that the framework reliably identifies individual sensorimotor and spectral parameters. Second, we use this framework to compare four HRTF template interpolation methods, showing that full-sphere spatial coverage and high-frequency spectral fidelity are the primary determinants of template quality, while the specific interpolation algorithm is secondary. Together, these results show that standard model-based statistical methods can address both fundamental questions in spatial hearing and applied problems such as perceptual HRTF evaluation. An open-source Python implementation is released alongside this work.
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