# Room Impulse Response Generation Conditioned on Acoustic Parameters

arXiv: https://arxiv.org/abs/2507.12136 · HTML: https://mlsanity.com/paper/2507.12136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model](https://mlsanity.com/paper/2608.14097/) (2026)
- [PromptReverb: Multimodal Room Impulse Response Generation Through Latent Rectified Flow Matching](https://mlsanity.com/paper/2510.22439/) (2025)
- [SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements](https://mlsanity.com/paper/2605.10398/) (2026)

## Abstract (arXiv)

The generation of room impulse responses (RIRs) using deep neural networks has attracted growing research interest due to its applications in virtual and augmented reality, audio postproduction, and related fields. Most existing approaches condition generative models on physical descriptions of a room, such as its size, shape, and surface materials. However, this reliance on geometric information limits their usability in scenarios where the room layout is unknown or when perceptual realism (how a space sounds to a listener) is more important than strict physical accuracy. In this study, we propose an alternative strategy: conditioning RIR generation directly on a set of RIR acoustic parameters. These parameters include various measures of reverberation time and direct sound to reverberation ratio, both broadband and bandwise. By specifying how the space should sound instead of how it should look, our method enables more flexible and perceptually driven RIR generation. We explore both autoregressive and non-autoregressive generative models operating in the Descript Audio Codec domain, using either discrete token sequences or continuous embeddings. Specifically, we have selected four models to evaluate: an autoregressive transformer, the MaskGIT model, a flow matching model, and a classifier-based approach. Objective and subjective evaluations are performed to compare these methods with state-of-the-art alternatives. Results show that the proposed models match or outperform state-of-the-art alternatives, with the MaskGIT model achieving the best performance.