mandipgoswami/rirmega

🔖 How to Cite

If RIR-Mega helps your research, please cite both the paper and the dataset:

Paper Goswami, M. (2025). RIR-Mega: A Large-Scale Room Impulse Response Corpus with Benchmarks for Industrial and Building Acoustics. arXiv:2510.18917. https://arxiv.org/abs/2510.18917

Dataset Goswami, M. (2025). RIR-Mega Dataset (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.17387402

@misc{goswami2025rirmega,
  title        = {RIR-Mega: A Large-Scale Room Impulse Response Corpus with Benchmarks for Industrial and Building Acoustics},
  author       = {Goswami, Mandip},
  year         = {2025},
  eprint       = {2510.18917},
  archivePrefix= {arXiv},
  primaryClass = {cs.SD},
  url          = {https://arxiv.org/abs/2510.18917}
}

@dataset{goswami_2025_rirmega_zenodo,
  author       = {Goswami, Mandip},
  title        = {RIR-Mega Dataset},
  year         = {2025},
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.17387402},
  url          = {https://doi.org/10.5281/zenodo.17387402}
}

Goswami, M. (2025). RIR-Mega: A Large-Scale Room Impulse Response Corpus with Benchmarks for Industrial and Building Acoustics. arXiv:2510.18917. https://arxiv.org/abs/2510.18917

Goswami, M. (2025). RIR-Mega Dataset (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17387402

RIR-Mega

Paper | Code | Project page (Zenodo)

RIR-Mega provides thousands of simulated room impulse responses for research in dereverberation, robust speech recognition, and acoustic scene analysis. This Hugging Face release hosts a lightweight, representative subset — 1 000 linear-array and 3 000 circular-array RIRs — for quick exploration, tutorials, and reproducible baselines.

The complete 50 000-RIR archive is permanently preserved on Zenodo and described in the accompanying paper:

🤗 Subset for streaming: (https://huggingface.co/datasets/mandipgoswami/rirmega) 📦 Technical Paper: (arxiv.org/abs/2510.18917)

✨ What’s inside

• data/ — RIR audio and metadata/metadata.csv (compact schema)
• rirmega/dataset.py — Hugging Face Datasets loader
• benchmarks/rt60_regression/ — a lightweight RT60 regression baseline
• scripts/ — utilities (validation, checksums, mini subset)
• (optional) data-mini/ — tiny subset for quick demos and Spaces

Contents

Folder	Description
data/audio/linear	1 000 RIRs simulated for linear microphone arrays
data/audio/circular	3 000 RIRs simulated for circular arrays
data/metadata/metadata.csv / .parquet	Compact schema linking each file to acoustic metrics and simulation parameters
rirmega/dataset.py	Hugging Face Datasets loader (supports streaming)
benchmarks/rt60_regression/	Baseline RT60 regression example
scripts/	Validation + checksum utilities
figs/	Overview and validation plots for reference

📦 Schema (compact)

Column	Meaning
id	unique identifier
family	“linear” or “circular”
split	train / valid / test
fs	sampling rate (Hz)
wav	relative path to audio file
room_size, absorption, max_order	simulation parameters
metrics	JSON string with rt60, drr_db, c50_db, c80_db, and band-limited RT60s
rng_seed	random seed for reproducibility

🚀 Getting started

from datasets import load_dataset
ds = load_dataset("mandipgoswami/rirmega", trust_remote_code=True)
print(ds["train"][0]["audio"])       # lazy-loads waveform
print(ds["train"][0]["rt60"])        # scalar metadata

For streaming or partial download:

ds = load_dataset("mandipgoswami/rirmega", streaming=True)

🧪 Baseline: RT60 regression

Lightweight features + RandomForest to predict RT60-like targets from RIR signals.

python benchmarks/rt60_regression/train_rt60.py

pip install soundfile numpy pandas scikit-learn
python benchmarks/rt60_regression/train_rt60.py
# or choose a specific target key present in `metrics`
python benchmarks/rt60_regression/train_rt60.py --target rt60

Default target search order:

Technical Validation

A random subset of 1 000 samples was analyzed for internal consistency. The RT60 values derived from Schroeder energy decay curves correlated strongly with the metadata values:

Metric	Correlation	MAE (s)	RMSE (s)
RT60 (metadata vs EDC)	0.96	0.013	0.022

Reference numbers (example)

• Train/Valid used: 36,000 / 4,000 (auto 10% valid)
• Metric: MAE = 0.013 s, RMSE = 0.022 s (auto target)

🏅 Leaderboard (RT60 regression)

Date	Team / Author	Method	Target	Train/Valid	MAE (s)	RMSE (s)	Seed	Code
2025-10-19	Baseline (RIR-Mega)	RF on light feats	auto	36k / 4k	0.013	0.022	0	benchmarks/rt60_regression

📫 Submit a result: Open a PR adding a row (see Submitting).

📤 Submitting

See SUBMITTING.md for rules and a PR template. Minimum info:

• Command (incl. --target if used), seed, dataset tag (e.g., v1.0.0)
• Train/Valid sizes used
• MAE (s) and RMSE (s)
• Link to code (repo, gist, or HF Space)