Published August 26, 2025 | Version 1.0

Sensing the Forest - Natural Soundscape Dataset - Part 2/2

  • 1. ROR icon De Montfort University
  • 2. ROR icon Queen Mary University of London

Contributors

Hosting institution (2):

Project member:

  • 1. Forest Research
  • 2. Freesound

Description

This dataset contains natural soundscape recordings from a woodland near Alice Holt Lodge Pond, Surrey, UK, collected as part of the AHRC-funded Sensing the Forest project (AH/X011585/2). Recordings were made automatically four times a day aligned to solar time — at sunrise, solar noon, sunset, and the midpoint between sunset and sunrise.

The recording system is a solar-powered DIY Raspberry Pi audio streamer designed by Luigi Marino. Prior to 28 February 2025, recordings used MEMS microphones; from that date onward, audio quality improved significantly following an upgrade to RØDE LavalierGO microphones with a RØDE AI-Micro audio interface.

The woodland is dominated by Corsican pine (planted as a crop in 1992) with mixed broadleaf species including oak, sweet chestnut, birch, and willow. Wildlife present includes roe deer, muntjac deer, bats, and a variety of bird species such as common chiffchaff, coal tit, Eurasian wren, European robin, long-tailed tit, Eurasian blue tit, Eurasian treecreeper, Eurasian siskin, buzzard, and tawny owl.

The full dataset is divided into two parts due to file size constraints. This record contains Part 2 of 2, covering recordings from March 2025 to August 2025. Please refer to the linked companion record for Part 1 (August 2024  –March 2025). All audio files are in WAV format and are released under Creative Commons 0 (CC0 1.0 — Public Domain). The original sounds are also available on Freesound at: https://freesound.org/people/sensingtheforest/packs/42937/

For more information about the project, visit: https://sensingtheforest.github.io

Files

42937__sensingtheforest__natural-soundscape-dataset-part-2.zip

Files (20.7 GB)

Name Size
md5:01feafa071d8dd2e5f1a60df362c6cc3
20.7 GB Preview Download
md5:3e17209751cb59dbf10d2cadd78f00bb
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Additional details

Related works

Is part of
Dataset: 10.5281/zenodo.18909809 (DOI)

Funding

Arts and Humanities Research Council
Sensing the Forest - Let the Forest Speak using the Internet of Things, Acoustic Ecology and Creative AI AH/X011585/2

References