PromptReverb: Multimodal Room Impulse Response Generation Through Latent Rectified Flow Matching

TL;DR

PromptReverb is a novel two-stage generative framework that synthesizes high-quality, full-band room impulse responses from natural language descriptions, improving perceptual quality and acoustic accuracy for immersive virtual environments.

Contribution

It introduces a combined variational autoencoder and diffusion transformer approach for flexible, accurate RIR generation from diverse input modalities.

Findings

Achieves 8.8% mean RT60 error, outperforming baselines with -37%.

Produces more realistic room-acoustic parameters.

Generates high-quality RIRs suitable for virtual reality and architectural acoustics.

Abstract

Room impulse response (RIR) generation remains a critical challenge for creating immersive virtual acoustic environments. Current methods suffer from two fundamental limitations: the scarcity of full-band RIR datasets and the inability of existing models to generate acoustically accurate responses from diverse input modalities. We present PromptReverb, a two-stage generative framework that addresses these challenges. Our approach combines a variational autoencoder that upsamples band-limited RIRs to full-band quality (48 kHz), and a conditional diffusion transformer model based on rectified flow matching that generates RIRs from descriptions in natural language. Empirical evaluation demonstrates that PromptReverb produces RIRs with superior perceptual quality and acoustic accuracy compared to existing methods, achieving 8.8% mean RT60 error compared to -37% for widely used baselines and yielding more realistic room-acoustic parameters. Our method enables practical applications in virtual reality, architectural acoustics, and audio production where flexible, high-quality RIR synthesis is essential.