Icassp artifact evaluation

ICASSP Artifact Evaluation

Use this for evidence packaging around ICASSP. There is no formal artifact-evaluation committee or badge at ICASSP as of the 2026 cycle; artifacts are voluntary and their value is in review credibility and post-publication impact. Because review is single-blind, a repository can carry your name from day one — no anonymous mirror is required.

Artifact plan

• Decide what evidence supports the four-page claim: training and evaluation code, the exact dataset splits or trial lists, the scoring script, model checkpoints, seeds, logs, and a handful of qualitative samples (audio, spectrogram, image, or signal plot).
• Keep the decision-critical numbers reproducible from the released package; a reviewer who cannot regenerate the headline metric discounts it.
• Ship a minimal reproduction map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and any known nondeterminism (GPU kernels, thread counts).
• For restricted corpora you cannot redistribute, provide enough provenance and preprocessing to reproduce from the licensed source without violating the data-use terms.
• Because links can be public, put the repository URL in the paper and test it from a logged-out browser before submission.

What ICASSP evidence reviewers open first

Claim type	First artifact inspected	Common failure caught
Recognition/detection accuracy	Scoring script + dataset split	WER/error computed with an undocumented text-normalization or split
Enhancement / separation gain	Reference-aligned SI-SDR/PESQ/STOI scorer	Metric computed with mismatched reference or windowing
Speaker / biometric verification	Trial list + EER/minDCF scorer	Non-standard trials that inflate the score
Image/signal restoration	PSNR/SSIM script + test set	Metric on a different crop, bit depth, or borders
Real-time claim	Latency/RTF measurement harness	Feasibility asserted, never measured

Signal-processing reviewers will rerun a scorer on a provided output far more readily than they will retrain a model, so make the measurement path turnkey before polishing training code.

Worked vignette: packaging a separation result

A hypothetical submission reports source-separation gain on a two-speaker mixture set.

• Ship the mixture generation as one parameterized script (seed, SNR range, corpus source), not as constants buried in a notebook, so a reviewer can regenerate the exact test mixtures.
• Include the reference-aligned SI-SDR scorer with its settings; separation numbers are meaningless if the alignment and permutation handling differ.
• Emit result tables directly from logged outputs so the paper's numbers and the artifact's numbers cannot drift apart.
• Provide five listenable example mixtures with their separated outputs; a reviewer often judges perceptual quality from these before reading the metric table.

Turnkey scoring stub

# One command should reproduce the headline metric from released outputs.
python3 score.py \
  --hyp outputs/dev.hyp \
  --ref data/dev.ref \
  --metric si-sdr \
  --config configs/scoring.yaml   # window, alignment, permutation policy pinned here
# Expected: dev SI-SDR = <value in Table 1> (mean over 3 seeds)

Calibration anchors

• Assume only the README and one entry script get opened; design the package so the top-level README reproduces the main number in one command.
• Do not confuse a released toolkit with an artifact: ICASSP reviewers want the exact recipe that produced this paper's numbers, not a general library dump.
• Formats and sizes for any uploaded supplementary media are cycle-specific; verify against the current paper kit rather than past years.

Output format

[Artifact role] public release / demo samples / scoring package
[Contents] code / data-split / scorer / checkpoints / seeds / samples
[Reproduction level] turnkey / scripted / descriptive / weak
[Measurement risks] metric ruler / split / alignment / normalization
[Fixes before release] <ordered list>