{
  "abstract": "Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis. This paper introduces a Time-Warping Estimation Trainable (TWET) model for estimating timewarping functions from a single observation. The proposed approach formulates time-warping estimation as a stationarization problem in the wavelet domain and leverages a hierarchical dilated convolutional architecture to estimate the time-warping functions. A differentiable stationarity criterion is introduced for end-to-end optimization. TWET is compared with existing approaches. Experimental results show improved deformation reconstruction accuracy together with significantly reduced computation time, making the framework compatible with low-latency applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: 2026 IEEE International Workshop on Machine Learning for Signal Processing, Sep 2026, Atlanta, France",
  "arxiv": "2609.16796",
  "author_total": 2,
  "authors": [
    {
      "name": "Corentin Presvôts"
    },
    {
      "name": "Adrien Meynard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d000s9v90456z.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16796",
  "fulltext_url": null,
  "id": "d000s9v90456z",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16796",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1711|Music and Audio Processing"
    ]
  ],
  "openalex": "W7213420730",
  "pdf_url": "https://arxiv.org/pdf/2609.16796",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d000s9v90456z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "2609.16796",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16796"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Time-warping estimation via stationarity-based learning of the de-warped signal",
  "title_text": "Time-warping estimation via stationarity-based learning of the de-warped signal",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/d000s9v90456z",
  "venue": "arXiv",
  "versions": []
}