用扩散模型先验估计混响环境中的头相关传输函数。
HRTF Estimation using a Score-based Prior
- 基于扩散模型构建声学先验，指导混响中HRTF估计。
- 在真实语音激励下，性能超越最优基线系统。
- 有效捕捉高频成分的个体差异，适合个性化音频应用。
本文提出一种基于数据驱动先验的头相关传输函数（HRTF）估计方法，该先验由得分型扩散模型提供。在混响环境中，利用自然激励信号（如人声）进行估计，同时通过优化基于房间声学统计特性的参数化混响模型，联合估计房间脉冲响应与HRTF。基于得分型HRTF先验与对数似然近似，建模给定混响测量值和激励信号下的后验分布。实验表明，该方法显著优于多个基线，包括一个基于训练集中最小距离选择最优HRTF的虚拟最优推荐系统。尤其在高频频段内容的个体差异建模方面表现突出。
原文摘要 · Abstract (English)
We present a head-related transfer function (HRTF) estimation method which relies on a data-driven prior given by a score-based diffusion model. The HRTF is estimated in reverberant environments using natural excitation signals, e.g. human speech. The impulse response of the room is estimated along with the HRTF by optimizing a parametric model of reverberation based on the statistical behaviour of room acoustics. The posterior distribution of HRTF given the reverberant measurement and excitation signal is modelled using the score-based HRTF prior and a log-likelihood approximation. We show that the resulting method outperforms several baselines, including an oracle recommender system that assigns the optimal HRTF in our training set based on the smallest distance to the true HRTF at the given direction of arrival. In particular, we show that the diffusion prior can account for the large variability of high-frequency content in HRTFs.
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