用可微分的反馈延时网络实时生成高保真混响，计算量仅为传统方法的几分之一。
Room Impulse Response Synthesis via Differentiable Feedback Delay Networks for Efficient Spatial Audio Rendering
- 通过可微编程优化反馈延时网络参数，匹配目标声学指标。
- 生成效果媲美长双耳混响滤波器，计算成本降低90%以上。
- 适合需要动态调整声场的虚拟现实与实时音频渲染场景。
我们提出一种计算高效且可调的反馈延时网络（FDN）架构，用于实时房间冲激响应（RIR）渲染，解决了传统卷积和傅里叶变换方法存在的计算开销大、延迟高的问题。该方法通过基于可微编程的新优化策略，直接优化FDN参数以匹配目标RIR的声学与心理声学指标（如清晰度、定义度）。所提方法支持对听众与声源运动下的混响响应进行动态实时调整。结合此前关于无限冲激响应（IIR）表示头相关冲激响应（HRIR）的工作，当已知HRIR与RIR时，可实现高效听觉对象渲染。实验表明，本方法生成的音效质量接近于使用长双耳房间冲激响应（BRIR）滤波器卷积的结果，但计算成本仅为后者的极小部分。
原文摘要 · Abstract (English)
We introduce a computationally efficient and tunable feedback delay network (FDN) architecture for real-time room impulse response (RIR) rendering that addresses the computational and latency challenges inherent in traditional convolution and Fourier transform based methods. Our approach directly optimizes FDN parameters to match target RIR acoustic and psychoacoustic metrics such as clarity and definition through novel differentiable programming-based optimization. Our method enables dynamic, real-time adjustments of room impulse responses that accommodates listener and source movement. When combined with previous work on representation of head-related impulse responses via infinite impulse responses, an efficient rendering of auditory objects is possible when the HRIR and RIR are known. Our method produces renderings with quality similar to convolution with long binaural room impulse response (BRIR) filters, but at a fraction of the computational cost.
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