icassp-experiments
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Experiment audit
- Match the metric to the task law
- What experiments are for at this venue
- Ablation and sweep stub
- Vignette: a dereverberation paper
- Reporting floor
- Output format
What does the icassp-experiments skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.