Datasets:

mandipgoswami
/

RIR-Bench-Hard

This directory is the adoption magnet: a self-contained scorer plus the exact submission format, so anyone can evaluate a dereverberation / room- acoustics system against RIR-Bench-Hard and report a comparable number.

evaluate.py depends on numpy, scipy, soundfile only — no deep-learning stack required.

pip install numpy scipy soundfile pandas   # pandas optional (parquet reader)
python evaluate.py --metadata ../metadata.parquet --pred my_system.csv

What is scored

For every benchmark item the reference metadata carries four acoustic descriptors. A submission is scored by the Mean Absolute Error (MAE) of its recovered descriptors against the reference, reported per category, for the test_hard split, and overall. Only rows with valid == True are scored (5 of 2600 rows are flagged non-physical and excluded; see the dataset card's Quality-control section).

Metric	Definition
`t60_measured`	Reverberation time (s). Schroeder backward-integration EDC, line fit on the −5 dB → −35 dB region (T30), extrapolated to 60 dB.
`drr_db`	Direct-to-reverberant ratio (dB). Energy in a ±2.5 ms window around the peak vs. the remainder.
`c50_db`	Clarity (dB). `10·log10( E[0–50 ms] / E[50 ms–end] )`, referenced to the direct arrival, clamped to ±60 dB.
`edt_s`	Early decay time (s). Slope over 0 → −10 dB, extrapolated to a 60 dB decay.

The estimator source is duplicated verbatim inside evaluate.py (and lives in scripts/generate_rirs.py) so your scores use the identical definitions that built the benchmark.

Two submission modes

1. Predictions CSV (recommended — framework-agnostic)

Produce one row per benchmark id with your system's estimate of whichever metrics it recovers. Unknown metrics may be omitted (they are skipped, not penalised).

id,t60_measured,drr_db,c50_db,edt_s
long_t60_0000,1.19,-11.1,1.9,1.20
long_t60_0001,1.28,-5.0,3.0,0.95
...

python evaluate.py --metadata ../metadata.parquet --pred my_system.csv --out my_results.csv

2. Enhanced-audio directory (signal-level)

Drop your enhanced / dereverberated outputs as <id>.wav in a directory. The harness recomputes the four metrics on your audio with the benchmark's own estimators and reports the same MAE table.

python evaluate.py --metadata ../metadata.parquet --enhanced-dir my_outputs/

Identity baseline (default)

With neither flag, the harness runs the identity (do-nothing) baseline: it recomputes the metrics on the reference WAVs. Because the reference metrics were derived from those WAVs, this defines the measurement floor (near-zero MAE, limited only by int16 storage quantisation). The seeded result ships as baseline_results.csv so the leaderboard has a value on day one.

Submitting to the leaderboard

Open a discussion / PR on the dataset repo with:

your --out results CSV (the per-bucket MAE table),
a one-line system description and a link to code or a paper,
which mode you used (predictions CSV or enhanced-audio).

Report the test_hard overall MAE as your headline number — that split is weighted toward the longest-T60 and lowest-DRR samples where dereverberation and far-field ASR break.