RIRmega v2 — Dataset card (Hugging Face)

This folder holds the v2 artifacts intended for the Hugging Face dataset mandipgoswami/rirmega. When published, use revision v2.0.0 for the v2 release.

Dataset description

RIRmega v2 extends the existing RIRmega v1 dataset with:

• A versioned metadata schema (metadata_v2.parquet) with acoustic metrics (RT60, DRR, C50, C80, D50, EDT), quality-control grades, and provenance.
• A QC report (qc_report.parquet) with checks and outlier scores.
• Official splits in splits/: random, unseen_room, unseen_distance (train/validation/test).

All v2 fields are derived from v1 or computed from the RIR waveforms; no private data is assumed.

v1 vs v2 changelog

Aspect	v1	v2
Metadata	Compact CSV/JSON (id, family, split, fs, metrics)	Parquet with full schema (see below)
QC	None	Grades A/B/C and qc_report.parquet
Splits	train/valid/test only	+ unseen_room, unseen_distance, random
Metrics	In JSON when present	Computed from RIR when missing (Schroeder, DRR, C50/C80/D50)

Schema (metadata_v2.parquet)

Column	Type	Description
sample_id	string	Unique identifier
sample_rate	int	Sampling rate (Hz)
rir_length_samples	int	Length of RIR
onset_sample	int	Estimated onset
room_id	string	Room or pseudo-room bucket
src_xyz, mic_xyz	string/JSON	Source/receiver coordinates if available
distance_m	float	Source–receiver distance if available
RT60_T20_s, RT60_T30_s	float	Reverberation time
EDT_s	float	Early decay time
DRR_dB	float	Direct-to-reverberant ratio
C50_dB, C80_dB	float	Clarity indices
D50	float	Early energy fraction (0–1)
band_rt60_octave	string	JSON band-limited RT60 when available
qc_grade	string	A / B / C
qc_flags	string	JSON list of flags
generator_version	string	e.g. 2.0.0
provenance	string	e.g. v1_rirmega_hf

Splits

• random: Random train/validation/test for baseline sanity.
• unseen_room: Test set = rooms held out by room_id.
• unseen_distance: Test set = distance buckets held out.

Each split is a JSON file with keys train, validation, test (lists of sample_id).

Evaluation tasks

• Parameter estimation: Predict RT60 (T20), DRR, C50 from RIR (regression). Metrics: MAE, RMSE, R²; bootstrap 95% CIs.
• Baselines: classical (features + Ridge/RF), 1D CNN, 1D Transformer. See the GitHub repo for code and results.

Quickstart (load v2 from Hugging Face)

from datasets import load_dataset
# Load v1 (audio); v2 metadata is in parquet files in the repo
ds = load_dataset("mandipgoswami/rirmega", revision="v2.0.0", trust_remote_code=True)
# If v2 parquet are uploaded as data files:
# import pandas as pd
# meta = pd.read_parquet("metadata_v2.parquet")  # from repo files

For full v2 pipeline (build, QC, eval, paper), clone and run:

git clone https://github.com/mandip42/rirmega-v2-release.git
cd rirmega-v2-release && pip install -e . && python scripts/release_v2.py

Citation

If you use RIRmega v2, please cite the dataset and the paper:

@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}
}

@misc{githubrirmegav2,
  author = {Goswami, Mandip},
  title  = {rirmega-v2-release},
  year   = {2025},
  url    = {https://github.com/mandip42/rirmega-v2-release}
}

License

Metadata and QC artifacts: CC BY 4.0 where applicable. RIR audio content: same license as the upstream RIRmega v1 dataset (see v1 dataset card) .