icassp-related-work

Business

Use when positioning an ICASSP submission across the signal-processing literature — IEEE SPS journals (TSP, TASLP, SPL, TIP and siblings), sibling conferences (Interspeech, ICIP, EUSIPCO, WASPAA), and ML venues, citing normally under single-blind review, and sharpening the technical delta against the nearest current-cycle work in four pages.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/ICASSP-Skills/skills/icassp-related-work/SKILL.md

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ICASSP Related Work

Use this to audit novelty and positioning. ICASSP is single-blind, so you cite normally — no anonymized references, no hiding your own prior work. Reopen the current call only for dual-submission and prior-publication rules. The four-page limit makes related work a precision instrument, not a survey.

Positioning checks

Separate the signal-processing novelty (a new estimator, transform, representation, objective, architecture, or resource) from an engineering improvement.
Compare against both the IEEE SPS journal literature and the conference literature; a reviewer from the relevant technical committee knows both.
Position against the nearest current-cycle work, not a five-year-old baseline; ICASSP reviewers work in the subfield and notice a stale comparison immediately.
Because review is single-blind, cite your own prior work in normal first person and make the incremental delta explicit rather than obscuring it.

Literature lanes table

Lane	Typical venues	What ICASSP reviewers check
SPS journals	IEEE TSP, TASLP, SPL, TIP, TIFS, TMM, TCI, TSIPN, JSTSP, OJSP	Whether the closest journal result is acknowledged and the delta stated
ICASSP + IEEE conferences	prior ICASSP, ICIP, ICME, SLT, ASRU	Whether the nearest conference method is compared or distinguished
Speech/audio siblings	Interspeech, WASPAA, EUSIPCO, DCASE	Whether a paper mis-attributed to ICASSP actually lives here
ML venues	NeurIPS, ICML, ICLR, AAAI	Whether generic-ML priors are credited without claiming their generality

A bibliography that cites only ML venues and no SPS journals tells a committee reviewer the paper may be rediscovering a known signal-processing result — a recognizable weakness that benchmark strength does not repair.

The sibling-venue attribution trap

Signal-processing landmarks are scattered across venues, and misattributing one is a credibility hit. Common confusions to check before citing:

Papers you think are ICASSP that are actually Interspeech (much of the speech-synthesis and self-supervised-speech canon) or journal papers (TASLP separation work).
ICIP for image results and EUSIPCO/WASPAA for European and audio-workshop results.
Verify each cited venue against dblp (conf/icassp/, journals/) rather than trusting memory; see ../../resources/exemplars/library.md for the guard list.

Positioning vignette

Suppose the paper proposes a low-complexity beamformer for a hearing device. Its nearest neighbors: a TASLP paper with higher complexity, a prior ICASSP paper with a similar structure but no latency budget, and an ML paper with a black-box network. The novelty sentence should name all three contrasts — lower complexity than the journal method, an explicit latency budget the prior ICASSP work lacked, and interpretability the black-box model lacked — in one or two sentences, because four pages cannot afford a paragraph per contrast.

Concurrent-work judgment

Independently concurrent arXiv work: cite neutrally, state the technical difference, and avoid priority claims a reviewer cannot verify.
Your own workshop or prior-conference version: cite it in first person and state exactly what this paper adds, since single-blind review does not require hiding it.
If a venue's archival status is unclear for dual-submission purposes, declare the overlap rather than gambling on an organizer's interpretation.

Output format

[Novelty type] estimator / transform / representation / objective / architecture / resource
[Nearest 3 works] <work -> venue (verified) -> technical delta>
[Lane coverage] SPS journals / ICASSP+conf / siblings / ML — gaps?
[Attribution risk] <any venue to re-verify on dblp>
[Novelty sentence] <one ICASSP-ready contribution contrast>