A bird vocalisation dataset of birds in Uganda for automated bio-acoustic monitoring and analysis.

Artificial intelligence Bio acoustics Deep learning Machine learning Ornithology

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Jun 2024
Historique:
received: 28 02 2024
revised: 24 03 2024
accepted: 12 04 2024
medline: 6 5 2024
pubmed: 6 5 2024
entrez: 6 5 2024
Statut: epublish

Résumé

This paper is a description of a bird vocalisation dataset containing electronic recordings of birds in Uganda. The data was collected from 7 locations namely Bwindi impenetrable forest, Kibale forest national park, Matheniko game reserve, Moroto district, Kidepo National Park, Lake Mburo National Park and Murchison Falls National Park. The data was collected between May and June 2023. All together there are 570 recordings from 212 unique species amounting to more than 4 hours of audio. This represents a significant addition to the publicly available electronically recorded vocalisations for birds in Africa. The research was funded by Google Africa Research Collabs for the project entitled, "BASIS: Broad Avian Species Surveillance with Intelligent Sensing".

Identifiants

pubmed: 38708308
doi: 10.1016/j.dib.2024.110433
pii: S2352-3409(24)00402-5
pmc: PMC11067472
doi:

Types de publication

Journal Article

Langues

eng

Pagination

110433

Informations de copyright

© 2024 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Mark Abraham Magumba (MA)

Makerere University, Uganda, College of Computing and Information Sciences, P.O.Box 7062, Kampala, Uganda.

Tom Denton (T)

Google Inc, Uganda.

Mutesasira Bashir (M)

Makerere University, Uganda, College of Computing and Information Sciences, P.O.Box 7062, Kampala, Uganda.

Classifications MeSH