Head-related transfer function (HRTF) contains all the relevant cues that humans rely on to perceive the direction of a sound source, and its modeling has been widely studied in the past years. Motivated by the different degree of fluctuations in HRTF magnitude spectrum across frequency, in this paper, a mixed-order modeling approach in the spherical harmonic domain is proposed in which HRTF magnitude spectrum with low fluctuation is modeled by the low-order spherical harmonics and that with high fluctuation is modeled by the high-order spherical harmonics. Furthermore, a data-driven optimization approach is presented to determine the transition frequency that separates HRTF magnitude spectrum into two ranges with different degrees of fluctuations. The effectiveness of the proposed mixed-order modeling approach is examined using FABIAN HRTF database through listening tests. Results show that this mixed-order modeling approach enables to reduce the order of spherical harmonics to 8 in the low frequencies and 18 in the high frequencies without perceptual degradation in sound quality and localization.

头部相关传递函数（HRTF）包含了人类感知声源方向所依赖的所有相关线索，并且其建模在过去几年中得到了广泛研究。基于不同频率下HRTF幅度谱的不同程度的波动，本文提出了一种球谐域的混合阶建模方法，其中低波动的HRTF幅谱通过低阶球谐函数建模，并且具有高波动性的模型是由高阶球谐函数建模的。此外，提出了一种数据驱动的优化方法来确定将HRTF幅度谱分为具有不同程度波动的两个范围的过渡频率。使用FABIAN HRTF数据库通过听力测试来检验所提出的混合顺序建模方法的有效性。结果表明，这种混合阶建模方法可以将球形谐波的阶数在低频下降低到8，在高频下降低到18，而不会在音质和定位上造成明显的影响。