For university labs

1. What is verifiable about RIR-Mega

• A published, openly licensed RIR corpus (mandipgoswami/rirmega, CC-BY-NC-4.0) with a versioned acoustic-metadata schema.
• Documented parameter-estimation baselines and tooling (repo, paper).
• A paired clean↔reverberant ASR-robustness pipeline (whisper-rirmega-bench).

No superlatives are claimed here. Novelty assessment is left to peer review and to the independent evaluations below.

2. Open benchmark questions

• How closely do RIR-Mega's simulated acoustic descriptors match independently measured rooms (ACE/REVERB) across RT60, DRR and clarity, and where do they diverge?
• Which room/geometry regimes (very long T60, coupled rooms, near-field, low DRR) are under-represented or least faithful?
• Does training RT60/DRR estimators on RIR-Mega transfer to measured RIRs, and how large is the synthetic-to-real gap?
• How much does RIR-Mega-induced reverberation degrade modern ASR (Whisper family), and does a simple dereverberation front-end recover it?
• Are the published metadata metrics reproducible from the raw waveforms with an independent estimator, within a stated tolerance?

3. Most valuable independent evaluations

• Recompute the published metadata metrics from the raw waveforms and report agreement (Track A) — a direct validity check.
• Measure the synthetic-to-real gap against ACE/REVERB (Track B) using your licensed access.
• Quantify ASR degradation and dereverberation recovery on your own speech test set (Track C).

4. Reproduce a baseline in 5–15 minutes

The quickstart runs on shipped fixtures with no dataset download and no network. Scale up to the real dataset when ready.