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Room Impulse Response Synthesis via Differentiable Feedback Delay Networks for Efficient Spatial Audio Rendering

Armin Gerami, Ramani Duraiswami

TL;DR

This work tackles the challenge of real-time, low-latency spatial audio by introducing a differentiable optimization framework for room impulse response (RIR) rendering using a Feedback Delay Network (FDN). The method separates early reflections (via a delayed-sum network) from the reverberant tail (via a 16-loop FDN) and optimizes FDN parameters to match perceptual targets defined by $C$, $D$, $CT$, and $T_{30}$ using convex loss terms in a differentiable programming setting. The key contributions are direct mapping of early-reflection parameters, a convex, gradient-based optimization for FDN tuning, and empirical demonstrations showing substantial computational savings (about $53\times$ over convolution and $2.3\times$ over FFT-based methods) while preserving perceptual quality and real-time adaptability. This enables efficient, on-device BRIR rendering when combined with HRIR-IIR approaches, supporting dynamic and personalized spatial audio in AR/VR and edge devices.

Abstract

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.

Room Impulse Response Synthesis via Differentiable Feedback Delay Networks for Efficient Spatial Audio Rendering

TL;DR

This work tackles the challenge of real-time, low-latency spatial audio by introducing a differentiable optimization framework for room impulse response (RIR) rendering using a Feedback Delay Network (FDN). The method separates early reflections (via a delayed-sum network) from the reverberant tail (via a 16-loop FDN) and optimizes FDN parameters to match perceptual targets defined by , , , and using convex loss terms in a differentiable programming setting. The key contributions are direct mapping of early-reflection parameters, a convex, gradient-based optimization for FDN tuning, and empirical demonstrations showing substantial computational savings (about over convolution and over FFT-based methods) while preserving perceptual quality and real-time adaptability. This enables efficient, on-device BRIR rendering when combined with HRIR-IIR approaches, supporting dynamic and personalized spatial audio in AR/VR and edge devices.

Abstract

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.

Paper Structure

This paper contains 8 sections, 6 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Proposed Method
  4. Early Reflections
  5. Reverberant Tail
  6. Proposed Differentiable Optimization
  7. Experiments
  8. Conclusion

Figures (3)

  • Figure 1: Example room impulse response partitioned into the early reflections and the reverberant tail segments.
  • Figure 2: The employed design for applying the early reflections (top, delayed sum network), reverberant tail (middle, feedback delay network), and room impulse response (bottom, overall network) in the $Z$ domain. The $Z$ exponent represents delay. For binaural synthesis, the HRIR specific to the direction of each path should be applied for the early reflections, and in a general direction towards the source for the reverberant tail.
  • Figure 3: Magnitude of the actual room impulse response (top) and our synthesized room impulse response (middle) for the first $4000$ time steps. They both share the early reflections, and their reverberant tails follow an exponential decay. Their discrete Fourier transforms (bottom) have the same characteristics as well.