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PeaDetect dataset: curated audio recordings of Indian peafowl (Pavo cristatus) vocalizations for bioacoustic monitoring

  • Rukshani Puvanendran
  • , Asanka P. Sayakkara
  • , Sampath S. Seneviratne
  • , Titus Jayarathna
  • , Roshan G. Ragel
  • University of Vavuniya
  • University of Peradeniya
  • University of Colombo

Research output: Contribution to journalArticlepeer-review

Abstract

This data article describes PeaDetect, the first curated audio dataset specifically designed for detecting Indian peafowl (Pavo cristatus) vocalizations. The dataset contains 2,950 five-second audio clips (4.1 hours total duration), evenly balanced between peafowl presence (1,475 clips) and absence (1,475 clips) classes. Recordings were sourced from two public repositories: Xeno-Canto (332 original peafowl recordings from India and Sri Lanka, 2003-2024, other bird species vocalizations) and Freesound (environmental sounds and insects from Sri Lankan habitats). All audio was standardized to 44.1 kHz, 16-bit, stereo WAV format and segmented into 5-second clips. Each clip was manually validated by an ornithologist, with inter-annotator agreement assessed on 20% of the dataset (Cohen's kappa = 0.905). To prevent data leakage, source-aware 5-fold cross-validation splits are provided, ensuring all clips from the same original recording remain within the same fold. The dataset includes comprehensive metadata with geographic (87.1% India, 11.9% Sri Lanka, 1.0% other regions), temporal (2003-2024, all seasons), and acoustic specifications. This dataset supports research in bioacoustic monitoring, human-wildlife conflict mitigation, and machine learning for conservation.

Original languageEnglish
Article number113038
Number of pages12
JournalData in Brief
Volume67
DOIs
Publication statusPublished - Aug 2026
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Bioacoustics
  • Conservation technology
  • Dataset
  • Human-peafowl conflict
  • Machine learning
  • Soundscape ecology
  • Vocalization analysis

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