icassp-experiments
GitHub指导ICASSP实验设计与审计，确保指标与任务匹配、基线强劲、覆盖标准语料库。强调在四页限制内进行关键条件扫描和消融实验，报告多次运行的离散度，避免常见拒稿陷阱如指标错误或数据泄露。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-experiments -g -y
SKILL.md
Frontmatter
{
"name": "icassp-experiments",
"description": "Use when designing or auditing ICASSP experiments across signal-processing modalities — matching the metric to the task law (WER, SI-SDR, PESQ\/STOI, EER\/minDCF, PSNR\/SSIM, BER, RMSE), anchoring baselines to current strong methods and standard corpora, sweeping the operating condition, and reporting spread over runs within the four-page limit."
}
ICASSP Experiments
Use this before submission when the empirical story is not yet locked. ICASSP reviewers are subfield experts who know the right metric and the right baseline for your task, so the fastest route to rejection is the wrong ruler or a stale comparison. The four pages force a small number of decisive experiments, not a large number of weak ones.
Experiment audit
- Map each empirical claim to a specific table, figure, or condition sweep.
- Use the field-standard metric for the task; a novel or convenient metric invites the "that is not how this task is measured" review.
- Anchor to a current strong baseline and a standard corpus/benchmark, not to a weak or dated reference that flatters the result.
- Sweep the operating condition that matters (SNR, reverberation, bit rate, noise level); a single-condition number rarely convinces a signal reviewer.
- Report spread over runs (multiple seeds), and say in the caption whether bars are standard deviations, standard errors, or confidence intervals.
- Audit for train/test leakage, speaker/scene overlap across splits, and metric computed on the wrong crop, alignment, or normalization.
Match the metric to the task law
| Task | Standard metric(s) | Standard evaluation anchor |
|---|---|---|
| Speech recognition | WER / CER | LibriSpeech, WSJ, or task corpus with fixed split |
| Enhancement / separation | SI-SDR, PESQ, STOI | Matched mixture set, reference-aligned scorer |
| Speaker / language ID | EER, minDCF | Standard trial lists (e.g., VoxCeleb-style) |
| Sound event / audio tagging | mAP, F1, error rate | Fixed labeled set, defined operating point |
| Image / video restoration | PSNR, SSIM | Standard test set, defined borders and depth |
| Communications | BER / BLER vs SNR | Defined channel model and decoder |
| Estimation / detection | RMSE, ROC/AUC | Monte-Carlo trials, bound (Cramér-Rao) if apt |
Reporting the wrong metric family (e.g., classification accuracy for a separation paper) is a first-round reject pattern; match the ruler to the task before anything else.
What experiments are for at this venue
- ICASSP experiments exist to demonstrate a signal-processing mechanism works under realistic conditions, not to top a leaderboard by any margin. One clean condition sweep beats five extra datasets at a single point.
- The strongest design isolates the claimed mechanism with an ablation and shows it holds across the operating range, with the standard baseline drawn on the same axes.
- Where a theoretical bound exists (estimation, detection, coding), compare against it rather than only against another method.
Ablation and sweep stub
Fig. 2: metric vs condition (e.g., SI-SDR vs input SNR, 0-20 dB)
- proposed (mean ± sd over 3 seeds)
- strong baseline (same corpus, same scorer)
Table 1: ablation — remove one component at a time, same protocol
- full method | -component A | -component B | baseline
Report: corpus + split, scorer config, seeds, run count, hardware/runtime
Vignette: a dereverberation paper
A submission claims improved dereverberation. The matching plan: evaluate on a standard reverberant set with PESQ and STOI using a fixed scorer, sweep reverberation time (RT60) rather than reporting one room, ablate the key module, draw a current strong baseline on the same axes, and report the mean and spread over seeds — every panel tied to the claim it supports.
Reporting floor
- Seeds and run counts for every stochastic figure; captions state what the error bars are.
- The actual compute and, for real-time claims, the measured latency or real-time factor — not a feasibility assertion.
- Honest disclosure of the condition you did not test, so a reviewer does not infer you hid it.
Output format
[Experiment readiness] strong / adequate / weak
[Metric fit] task-matched? <metric -> task>
[Baseline] current-strong / standard-corpus? yes/no
[Condition sweep] present over <axis>? yes/no
[Missing evidence] <ablation / spread / baseline / condition>
[Decision-critical next run] <one experiment>
Version History
- 9f86f09 Current 2026-07-19 15:54
