ICASSP Reproducibility

Use this before submission and again before camera-ready. ICASSP has no reviewed supplement, so reproducibility rests on what the four pages state plus whatever you release publicly (which, under single-blind review, may be public immediately). The recurring ICASSP failure is not a missing repository — it is a number whose measurement cannot be reconstructed.

The evidence spine

Map each claim — an algorithm result, a theoretical bound, or an empirical metric — to a checkable location in the paper or the released package:

• For an empirical result: dataset and version, split or trial list, front-end/DSP settings, model, the exact scorer and its configuration, seeds, number of runs, and reported spread.
• For an estimation/detection result: the signal and noise model, the estimator, and the reference bound (e.g., Cramér-Rao) the result is compared against.
• For a real-time or embedded claim: hardware, latency or real-time factor, and memory.
• Explain any data you cannot release honestly, and describe how a reader could reproduce from the licensed source.

The scoring ruler is the thing that decays

Across ICASSP's modalities, the same trap recurs: the metric name is stated but the ruler behind it is not, so the number is unreproducible.

Ship the ruler, not just the model: a released checkpoint with no scorer configuration cannot reproduce the headline metric.

Front-end determinism

Signal papers decay silently through the front end. Pin the sample rate, framing, window function, FFT size, feature type, and any resampling. A change from a 25 ms to a 20 ms window, or a resampler swap, moves every downstream number without touching the model — and reviewers who reproduce will notice.

Degrees of reproducibility

• Turnkey — one command regenerates each reported metric from released outputs and seeds.
• Scripted — scripts exist but need documented manual steps or licensed-data access.
• Descriptive — prose detailed enough that a competent engineer could rebuild the pipeline.

For ICASSP, make the scoring path turnkey even when full training stays scripted; reviewers rerun scorers, not trainings. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.

Reproducibility stub

# Pin the environment and the ruler; regenerate the headline number.
pip install -r requirements.txt          # exact versions, including the DSP/feature lib
python3 run_eval.py --config configs/main.yaml --seed 1
python3 run_eval.py --config configs/main.yaml --seed 2
python3 run_eval.py --config configs/main.yaml --seed 3
python3 aggregate.py --runs runs/ --report mean_std   # matches Table 1 mean ± spread

Vignette: a keyword-spotting paper

A submission reports detection accuracy for a small-footprint keyword spotter. Its reproducibility spine: the corpus version and split, the feature front-end (sample rate, mel bins, window), the decision threshold and how it was set, seeds and run count, the on-device latency, and the exact scorer for the false-alarm/false-reject operating point — plus one honest sentence on the condition it was not evaluated under (e.g., far-field noise).

Output format

[Claim inventory] <claim -> checkable location>
[Scoring ruler] pinned / partial / missing
[Front-end] sample rate / framing / features pinned?
[Randomness] seeds + run count + reported spread
[Reproducibility level] turnkey / scripted / descriptive
[Fixes] <what must appear in the 4 pages vs the released package>