{
  "abstract": "A soundscape is composed of three types of sound: biophony (sounds made by animals), geophony (natural abiotic sounds) and anthropophony (sounds made by humans). A key research question in the field of soundscape ecology is how these components interact with each other, specifically how biophony responds to geophony and anthropophony. Nevertheless, as of today, there are not many analytical instruments that enable the distinct quantification of these elements. Recent machine learning (ML) approaches aim to support automated analysis but often rely on task-specific or clean data, limiting generalisation to noisy passive acoustic monitoring (PAM) recordings. This study presents a reproducible framework to build ML models for coarse soundscape classification and introduces CoarseSoundNet , a deep learning model trained to distinguish biophony, geophony, and anthropophony under realistic terrestrial PAM conditions. We systematically investigate model architectures, the influence of an additional training class, data composition, and evaluation strategies. Our findings suggest that model performance improves with additional PAM data, especially when similar to the target domain, and by introducing an explicit silence class during training. Post-hoc target-domain calibration employing class-specific decision thresholds further enhance performance, while incorporating minimum annotation durations yields higher scores for anthropophony and geophony. Error analyses exhibit challenges for anthropophony due to masking effects and confusions for silence and insect sounds for geophony and biophony. Finally, we conduct an ecological case study showing that pre-filtering recordings with CoarseSoundNet yields acoustic index trends broadly similar to ground-truth filtering, although the resulting improvements in their relationships with avian α -diversity were limited.",
  "abstract_shown": true,
  "affiliations": [
    "TUM University Hospital, CHI – Chair of Health Informatics, Ismaninger Str. 22, Munich, 81675, Bavaria, Germany; MCML – Munich Center for Machine Learning, Munich, Bavaria, Germany; Corresponding author at: TUM University Hospital, CHI – Chair of Health Informatics, Ismaninger Str. 22, Munich, 81675, Bavaria, Germany.",
    "TUM University Hospital, CHI – Chair of Health Informatics, Ismaninger Str. 22, Munich, 81675, Bavaria, Germany; MCML – Munich Center for Machine Learning, Munich, Bavaria, Germany",
    "University of Freiburg, Faculty of Biology, Geobotany, Schaenzlestr. 1, Freiburg, 79104, Baden-Württemberg, Germany",
    "TUM University Hospital, CHI – Chair of Health Informatics, Ismaninger Str. 22, Munich, 81675, Bavaria, Germany; MCML – Munich Center for Machine Learning, Munich, Bavaria, Germany; Imperial College London, GLAM – Group on Language, Audio, & Music, London, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Alexander Gebhard"
    },
    {
      "affs": [
        1
      ],
      "name": "Andreas Triantafyllopoulos"
    },
    {
      "affs": [
        2
      ],
      "name": "Dominik Arend"
    },
    {
      "affs": [
        2
      ],
      "name": "Sandra Müller"
    },
    {
      "affs": [
        2
      ],
      "name": "Svenja Schmidt"
    },
    {
      "affs": [
        2
      ],
      "name": "Michael Scherer-Lorenzen"
    },
    {
      "affs": [
        3
      ],
      "name": "Björn W. Schuller"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1sx0f66v3cd96.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecoinf.2026.104101",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1574954126005091",
  "id": "1sx0f66v3cd96",
  "issns": [
    "1574-9541",
    "1878-0512"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecoinf.2026.104101",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH540-549.5"
    ],
    [
      "lcc",
      "T58.5-58.64"
    ],
    [
      "openalex",
      "1711|Music and Audio Processing"
    ]
  ],
  "openalex": "W7161943945",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/1sx0f66v3cd96.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:56Z",
      "native_id": "8929000e33204b1093426ba6b47fcf8f",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecoinf.2026.104101"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "lcc:QH540-549.5",
      "subfield": "ecology"
    }
  ],
  "title": "CoarseSoundNet: Building a reliable model for ecological soundscape analysis",
  "title_text": "CoarseSoundNet: Building a reliable model for ecological soundscape analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:56Z",
  "url": "https://research.draconitic.com/paper/1sx0f66v3cd96",
  "venue": "Ecological Informatics",
  "versions": []
}