# MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients

arXiv: https://arxiv.org/abs/2507.09750 · HTML: https://mlsanity.com/paper/2507.09750/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio](https://mlsanity.com/paper/2304.01448/) (2023)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement](https://mlsanity.com/paper/2305.08227/) (2023)
- [URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement](https://mlsanity.com/paper/2406.04660/) (2024)
- [An empirical study of Conv-TasNet](https://mlsanity.com/paper/2002.08688/) (2020)
- [TS-RIR: Translated synthetic room impulse responses for speech augmentation](https://mlsanity.com/paper/2103.16804/) (2021)
- [Toward Universal Speech Enhancement for Diverse Input Conditions](https://mlsanity.com/paper/2309.17384/) (2023)
- [Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation](https://mlsanity.com/paper/2501.13250/) (2025)
- [Why does music source separation benefit from cacophony?](https://mlsanity.com/paper/2402.18407/) (2024)
- [A study on more realistic room simulation for far-field keyword spotting](https://mlsanity.com/paper/2006.02774/) (2020)
- [Overview of the L3DAS23 Challenge on Audio-Visual Extended Reality](https://mlsanity.com/paper/2402.09245/) (2024)
- [An objective evaluation of Hearing Aids and DNN-based speech enhancement in complex acoustic scenes](https://mlsanity.com/paper/2307.12888/) (2023)

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

- [Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement](https://mlsanity.com/paper/2510.23141/) (2025)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement](https://mlsanity.com/paper/2608.20971/) (2026)

## Abstract (arXiv)

We investigate the effects of four strategies for improving the ecological validity of synthetic room impulse response (RIR) datasets for monoaural Speech Enhancement (SE). We implement three features on top of the traditional image source method-based (ISM) shoebox RIRs: multiband absorption coefficients, source directivity and receiver directivity. We additionally consider mesh-based RIRs from the SoundSpaces dataset. We then train a DeepFilternet3 model for each RIR dataset and evaluate the performance on a test set of real RIRs both objectively and subjectively. We find that RIRs which use frequency-dependent acoustic absorption coefficients (MB-RIRs) can obtain +0.51dB of SDR and a +8.9 MUSHRA score when evaluated on real RIRs. The MB-RIRs dataset is publicly available for free download.