IEEE ICASSP Submission Strategy
Evaluates signal processing manuscripts for IEEE ICASSP conference fit, framing, evidence bars, and desk-reject risks.
Designed for researchers and engineers targeting the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), this skill provides systematic manuscript evaluation, venue fit analysis, and strategic positioning. It assesses whether papers meet ICASSP's expectations for mathematical signal modeling, robust baselines, and reproducible empirical validation. The skill flags common desk-reject risks, identifies critical evidence and ablation gaps, guides author rebuttals, and offers re-routing recommendations to sibling venues when appropriate.
主な機能
01Assists in reframing generic ML papers to highlight core mathematical and signal contributions
02Evaluates manuscript scope against ICASSP signal processing, speech, audio, and image standards
03Identifies critical evidence gaps, missing baselines, and insufficient ablation studies
04Provides comparative routing recommendations to neighboring venues like NeurIPS, ICML, and COLING
051,074 GitHub stars
06Flags venue-specific desk-reject risks including anonymity, formatting, and weak signal modeling
ユースケース
01Pre-submission audit to verify evidence depth, baseline comparisons, and formatting compliance for ICASSP
02Diagnosing reviewer fit issues and determining whether to target ICASSP or re-route to AI/NLP venues
03Reframing journal or arXiv preprints into concise, high-impact conference papers for signal processing reviewers