更新人耳听觉滤波器常数,基于新数据提升模型准确性
Auditory Filter Behavior and Updated Estimated Constants
- 基于近年听觉特性数据重新估算滤波器常数
- 发现相位与幅度特征比值对常数约束更有效
- 适用于多种滤波器类型,助力听觉模型优化
Gammatone类滤波器常用于模拟人耳听觉处理,但其滤波器常数多沿用数十年前的心理声学数据。本文提出基于最新报告的滤波器特性(如品质因数、品质因数与峰值群延迟比值)的特性驱动框架,厘清滤波器行为与底层常数的关系。通过尖锐滤波近似,分析在不固定滤波器阶数或指数时,滤波器可实现的行为范围。采用幅频与相频特性及其比值表征滤波器行为,揭示哪些特性对常数约束有效,哪些仅弱约束。研究结果扩展至多个可实现的Gammatone类滤波器,并结合近期生理与心理声学观测,推导出人耳听觉滤波器常数的约束与估计值。该框架支持任意特征规格的听觉滤波器设计,可系统评估滤波器特性变化对听觉模型、感知发现及依赖滤波器组技术的影响。
原文摘要 · Abstract (English)
Filters from the Gammatone family are often used to model auditory signal processing, but the filter constant values used to mimic human hearing are largely set to values based on historical psychoacoustic data collected several decades ago. Here, we move away from this long-standing convention, and estimate filter constants using a range of more recent reported filter characteristics (such as quality factors and ratios between quality factors and peak group delay) within a characteristics-based framework that clarifies how filter behavior is related to the underlying constants. Using a sharp-filter approximation that captures shared peak-region behavior across certain classes of filters, we analyze the range of behaviors accessible when the full degrees of freedom of the filter are utilized rather than fixing the filter order or exponent to historically prescribed values. Filter behavior is characterized using magnitude-based and phase-based characteristics and their ratios, which reveal which characteristics are informative for constraining filter constants and which are only weakly constraining. We show that these insights and estimation methods extend to multiple realizable filter classes from the Gammatone family and apply them, together with recent physiological and psychoacoustic observations, to derive constraints on and estimates for filter constants for human auditory filters. More broadly, this framework supports the design of auditory filters with arbitrary characteristic-level specifications and enables systematic assessment of how variations in filter characteristics influence auditory models, perceptual findings, and technologies that rely on auditory filterbanks.
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