{
  "abstract": "Passive acoustic monitoring produces far more bat recordings than experts can label. We show that simple model-generated pseudo-labels turn this surplus into effective supervision. We compare pseudo-labeling with other semi-supervised learning methods on an 18-species European corpus using only 10% of its training labels, then transfer the strongest approaches to South African field audio containing nine bat taxa and a nuisance class. Pseudo-labeling outperforms the other semi-supervised learning methods on every European measure, recovering up to 61.5% of the gap to full supervision. It transfers to field audio with gains of 10.69 points in species accuracy and 4.96 points in species macro-F1. We also introduce genus-aware smoothing, which directs uncertain target mass toward congeneric species. Combined with uniform smoothing, it reaches 79.16 species macro-F1, 4.73 points above hard targets. Simple pseudo-labels are therefore highly effective at this ecological data scale, while genus-aware targets inject useful biological structure at no annotation cost. https://code4conservation.github.io/UnlabeledEchoes/",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11986",
  "author_total": 2,
  "authors": [
    {
      "name": "Frank Fundel"
    },
    {
      "name": "Alexandra Howard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ttxwpax5y0t6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11986",
  "fulltext_url": null,
  "id": "8ttxwpax5y0t6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11986",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.SD"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1105|Bat Biology and Ecology Studies"
    ]
  ],
  "openalex": "W7212812673",
  "pdf_url": "https://arxiv.org/pdf/2609.11986",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ttxwpax5y0t6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:12Z",
      "native_id": "2609.11986",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11986"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition",
  "title_text": "Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:12Z",
  "url": "https://research.draconitic.com/paper/8ttxwpax5y0t6",
  "venue": "arXiv",
  "versions": []
}