{
  "annotation_id": "51b3cbb6-a893-4423-a086-debfe340bd5d",
  "date_created": "2026-03-02T18:00:59.871000Z",
  "date_modified": "2026-03-02T18:00:59.871000Z",
  "file_hash": "09ace047d450d265843a1f39893b6415afd9a8a64aaa74692d646819fecdb77c",
  "private": false,
  "record": {
    "abstract": "We study the performance of three different methods to automatically detect a\nchirp in background noise. (1) The standard deviation detector uses the\ncomputation of the signal to noise ratio. (2) The spectral covariance detector\nis based on the recognition of the chirp in the spectrogram. (3) The CASSANDRA\ndetector uses diffusion entropy analysis to detect periodic patterns in noise.\nAll three detectors are applied to an infrasound recording for detecting chirps\nproduced by sprites. The CASSANDRA detector provides the best trade off between\nthe false alarm rate and the detection efficiency.",
    "arxiv_id": "physics/0508141",
    "authors": [
      "M. Ignaccolo",
      "T. Farges",
      "E. Blanc",
      "M. Fullekrug"
    ],
    "categories": [
      "physics.data-an",
      "physics.ao-ph"
    ],
    "title": "Automated Chirp Detection with Diffusion Entropy: Application to   Infrasound from Sprites",
    "url": "https://arxiv.org/abs/physics/0508141"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "231c77f4-7418-4022-b688-88e59b9195ff",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}