{
  "annotation_id": "ef98a9e5-3d41-43af-b921-4c88f76a82be",
  "date_created": "2026-02-17T05:53:15.954000Z",
  "date_modified": "2026-02-17T05:53:15.954000Z",
  "file_hash": "7e8ea00b6fd2f85e5ddf2c9e4345c7668a96dc9870d5efe1d20ec9a2f137a082",
  "private": false,
  "record": {
    "abstract": "A lack of standardized datasets has long hindered progress in automatic intrapulse modulation classification (AIMC) - a critical task in radar signal analysis for electronic support systems, particularly under noisy or degraded conditions. AIMC seeks to identify the modulation type embedded within a single radar pulse from its complex in-phase and quadrature (I/Q) representation, enabling automated interpretation of intrapulse structure. This paper introduces AIMC-Spec, a comprehensive synthetic dataset for spectrogram-based image classification, encompassing 33 modulation types across 13 signal-to-noise ratio (SNR) levels. To benchmark AIMC-Spec, five representative deep learning algorithms - ranging from lightweight CNNs and denoising architectures to transformer-based networks - were re-implemented and evaluated under a unified input format. The results reveal significant performance variation, with frequency-modulated (FM) signals classified more reliably than phase or hybrid types, particularly at low SNRs. A focused FM-only test further highlights how modulation type and network architecture influence classifier robustness. AIMC-Spec establishes a reproducible baseline and provides a foundation for future research and standardization in the AIMC domain.",
    "arxiv_id": "2601.08265",
    "authors": [
      "Sebastian L. Cocks",
      "Salvador Dreo",
      "Feras Dayoub"
    ],
    "categories": [
      "cs.CV"
    ],
    "doi": "10.1109/ACCESS.2025.3645091",
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions",
    "url": "https://arxiv.org/abs/2601.08265",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ef6ecf1f-0216-4680-a140-99a30b2dcedf",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}