# Room Impulse Response Estimation in a Multiple Source Environment

arXiv: https://arxiv.org/abs/2305.15898 · HTML: https://mlsanity.com/paper/2305.15898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2023.

## Built on by (most cited first, 1 of 1)

- [Blind Spatial Impulse Response Generation from Separate Room- and Scene-Specific Information](https://mlsanity.com/paper/2409.14971/) (2024)

## Abstract (arXiv)

In real-world acoustic scenarios, there often are multiple sound sources present in a room. These sources are situated in various locations and produce sounds that reach the listener from multiple directions. The presence of multiple sources in a room creates new challenges in estimating the room impulse response (RIR) as each source has a unique RIR, dependent on its location and orientation. Therefore, issues of determining which RIR should be predicted and how to predict it arise, when the input signal is a mixture of multiple reverberated sources. To address these, we propose a new task of predicting a "representative" RIR for a room in a multiple source environment and present a training method to achieve this goal. In contrast to the model trained in a single source environment, our method shows robust performance, regardless of the number of sources in the environment.