用扩散模型根据人体数据生成个性化声学响应，提升沉浸音频真实感。
Towards HRTF Personalization using Denoising Diffusion Models
- 以人体测量数据为条件，用扩散模型生成个性化头相关脉冲响应。
- 生成效果达到当前最优水平，验证了方法可行性。
- 适合做沉浸式音频、虚拟现实的开发者与研究者参考。
头相关传输函数（HRTF）在沉浸式音频渲染中具有基础性应用，但其高度依赖个体差异，随耳部、头部和躯干形状变化显著，因此需个性化处理以实现精准双耳渲染。近期，去噪扩散概率模型（DDPM）作为一种生成学习技术，已被应用于多种信号处理任务。本文首次提出一种基于人体测量数据条件的DDPM方法，用于生成个性化头相关脉冲响应（HRIR，即HRTF的时域表示）。实验结果表明，该方法在性能上达到当前先进水平，验证了扩散模型在HRTF个性化中的可行性。
原文摘要 · Abstract (English)
Head-Related Transfer Functions (HRTFs) have fundamental applications for realistic rendering in immersive audio scenarios. However, they are strongly subject-dependent as they vary considerably depending on the shape of the ears, head and torso. Thus, personalization procedures are required for accurate binaural rendering. Recently, Denoising Diffusion Probabilistic Models (DDPMs), a class of generative learning techniques, have been applied to solve a variety of signal processing-related problems. In this paper, we propose a first approach for using DDPM conditioned on anthropometric measurements to generate personalized Head-Related Impulse Response (HRIR), the time-domain representation of HRTF. The results show the feasibility of DDPMs for HRTF personalization obtaining performance in line with state-of-the-art models.
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