icassp-topic-selection

GitHub

辅助判断论文是否适合ICASSP等信号处理会议。核心是识别贡献是否为信号处理原语,并针对语音工作提供ICASSP与Interspeech的路由决策指南,同时涵盖与其他相关会议的匹配建议。

ICASSP-Skills/skills/icassp-topic-selection/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

决定论文投稿目标会议 区分ICASSP和Interspeech的适用性 评估信号处理原语贡献

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-topic-selection -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-topic-selection -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@icassp-topic-selection

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-topic-selection -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

SKILL.md

Frontmatter
{
    "name": "icassp-topic-selection",
    "description": "Use when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech routing decision for speech work, plus routing to ICIP, EUSIPCO, WASPAA, SPS journals, or ML venues by identifying the signal-processing primitive of the contribution."
}

ICASSP Topic Selection

Use this before writing. ICASSP is the IEEE Signal Processing Society flagship, and its defining feature is breadth: speech and audio, image and video, communications and radar, sensor arrays, estimation and detection theory, and machine learning for signals all review under one roof. Speech is one track among many, not the identity of the venue. A project fits when its contribution changes a signal-processing primitive and can be proved in four pages.

Find the signal-processing primitive

Name what the contribution actually is:

  • A new estimator, transform, filter, or detector; a new representation or embedding; a new objective or inference procedure; a resource (corpus/benchmark); or a model-based-deep-learning result that changes a block of the signal chain.
  • If no signal-processing primitive exists — the paper is generic ML with no signal model — ICASSP is likely the wrong venue (see the re-route table).

The ICASSP-vs-Interspeech routing decision (read this for any speech paper)

Speech papers can go to either venue, and picking wrong costs a cycle. They differ on mechanics as much as on scope:

Dimension ICASSP (IEEE SPS) Interspeech (ISCA)
Scope All signal processing; speech is one track Speech and spoken language only
Culture Signal-processing methods, math-forward Speech-science + engineering, spoken-language focus
Format 4+1 (4 content pages + reference page) 4+1 (4 content + reference page), different template
Anonymity Single-blind — author list included Double-anonymous with a pre-deadline anonymity period
Deadline September (autumn) Late February (winter)
Portal CMS (cmsworkshops.com) Microsoft CMT
Proceedings IEEE Xplore ISCA Archive, open access

Decision rule: route to ICASSP when the contribution is a signal-processing method whose speech application is one instance (a new front-end, estimator, separation objective, or array technique), or when the September calendar and single-blind culture fit. Route to Interspeech when the contribution is fundamentally about spoken language — phonetics, prosody, dialogue, speech science, or a speech-specific model where the linguistic content is the point — or when the February calendar and double-blind norms fit. When both fit, let deadline and reviewer community decide.

Fit signal table (full ICASSP breadth)

Signal in the project ICASSP reading
New estimator/filter/detector with a signal model + evaluation Core fit — the house genre
Speech/audio method framed as a signal-processing mechanism Core fit (vs Interspeech if it is spoken-language-centric)
Image/video restoration, coding, or analysis Core fit (compare ICIP for image-specific work)
Communications, radar, array, or sensor signal processing Core fit
Generic deep net with strong benchmarks but no signal insight Better at NeurIPS/ICML/ICLR/AAAI
A finished, journal-length result with extensive theory An SPS journal (TSP/TASLP/SPL)

Re-route table

  • Generic ML, no signal model → NeurIPS, ICML, ICLR, AAAI.
  • Spoken-language-centric speech → Interspeech (or SLT/ASRU for those subfields).
  • Image-specific → ICIP; European/audio-workshop → EUSIPCO, WASPAA, DCASE.
  • Journal-length, finished → IEEE TSP, TASLP, SPL, TIP, or a sibling SPS journal; consider the OJSP-ICASSP track to present at ICASSP while publishing open-access in a journal.

Sharpening moves before committing

  • State the primitive in one sentence; if you cannot, the ICASSP framing does not yet exist.
  • Confirm the result fits four pages with a task-matched metric and a standard baseline; a claim that needs journal-length exposition should go to an SPS journal or the OJSP track.
  • For speech work, run the ICASSP-vs-Interspeech table explicitly rather than defaulting to whichever deadline is closer.
  • Subject-area emphasis drifts by cycle; scan the current EDICS list before final routing.

Output format

[Fit] strong ICASSP / possible ICASSP / better elsewhere
[Primitive] estimator / transform / representation / objective / resource / model-based-DL / none
[If speech] ICASSP vs Interspeech -> <decision + reason (scope/calendar/blinding)>
[Best venue] ICASSP / Interspeech / ICIP / EUSIPCO / WASPAA / SPS journal / ML venue
[Contribution sentence] <one signal-processing claim>
[Next action] <framing, experiment, or venue switch>

Version History

  • 9f86f09 Current 2026-07-19 15:54

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