NIIRF：用于 HRTF 上采样和个性化的神经 IIR 滤波器场
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
AI总结：
提出 NIIRF，用神经场预测级联 IIR 滤波器系数以实现 HRTF 空间上采样，并结合低秩自适应支持个性化，在稀疏测量下优于现有 NF 方法。
摘要：
头部相关传递函数（HRTF）对沉浸式音频十分重要，人们已研究其空间插值以对有限测量进行上采样。近来，将声源方向映射到 HRTF 的神经场（NF）受到关注。现有基于 NF 的方法侧重于根据给定声源方向估计 HRTF 的幅度，再将该幅度转换为有限脉冲响应（FIR）滤波器。我们提出神经无限脉冲响应滤波器场（NIIRF）方法，转而估计级联 IIR 滤波器的系数。IIR 滤波器模拟 HRTF 的模态特性，因此与 FIR 滤波器相比，只需更少系数即可很好地逼近它们。我们发现，该方法在多个数据集上能够匹配现有基于 NF 的方法的性能，甚至在测量稀疏时优于它们。我们还探索了使 NF 针对受试者个性化的方法，并通过实验发现低秩自适应是有效的。
英文摘要：
Head-related transfer functions (HRTFs) are important for immersive audio, and their spatial interpolation has been studied to upsample finite measurements. Recently, neural fields (NFs) which map from sound source direction to HRTF have gained attention. Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter. We propose the neural infinite impulse response filter field (NIIRF) method that instead estimates the coefficients of cascaded IIR filters. IIR filters mimic the modal nature of HRTFs, thus needing fewer coefficients to approximate them well compared to FIR filters. We find that our method can match the performance of existing NF-based methods on multiple datasets, even outperforming them when measurements are sparse. We also explore approaches to personalize the NF to a subject and experimentally find low-rank adaptation to be effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
