{"id":11705,"date":"2026-03-20T16:58:05","date_gmt":"2026-03-20T16:58:05","guid":{"rendered":"https:\/\/www.birds.cornell.edu\/ccb\/?page_id=11705"},"modified":"2026-03-20T16:59:48","modified_gmt":"2026-03-20T16:59:48","slug":"congo-soundscapes-public-database","status":"publish","type":"page","link":"https:\/\/www.birds.cornell.edu\/ccb\/elephant-listening-project\/congo-soundscapes-public-database\/","title":{"rendered":"Congo Soundscapes \u2013 Public Database"},"content":{"rendered":"\n

The Goal: expanding understanding of biodiversity through sound
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Background<\/h2>\n\n\n\n

Many of the most biodiverse ecosystems left on the planet are poorly studied, highly threatened, and in need of information that can be helpful in moving toward better protection and management. As part of the Elephant Listening Project\u2019s efforts to better understand the ecology of forest elephant movements, and the spatial and temporal pattern of poaching, we have recorded the rainforest\u2019s sounds 24\/7 at many places in Central Africa. But we only extract a tiny bit of the rich information that is contained in these continuous recordings, now representing more than 1 million hours of the sounds of birds, primates, insects, frogs, you name it. If it makes sound, we record it<\/em>.<\/p>\n\n\n\n

In order to make this rich acoustic data available to anyone who would like to mine it, we have partnered with Amazon Web Services<\/a> (AWS) to make sounds available in the cloud, using their object storage service, S3. While the raw sound files are available for download, AWS also provides access to flexible cloud computing resources in the form of EC2<\/a> instances. Especially after exploring a subset of the sound files we are making available, users may want to consider analyzing large portions of these data using an AWS EC2 instance because data transfer will be extremely fast between S3 and EC2 compared to downloading the data to a local machine. An additional benefit of using EC2 for analyses is access to instances with more powerful computing resources than a desktop or laptop. Depending on the type of sound analysis desired, working with the Nouabal\u00e9-Ndoki dataset can be extremely memory intensive, for example, running detectors or quantifying noise profiles. Just one possibility, for example, would be to use RStudio, with associated acoustic analysis packages (see Louis Aslett\u2019s detailed guide<\/a> for setting up RStudio on an AWS EC2 instance).<\/p>\n\n\n\n

The Sounds<\/h2>\n\n\n\n

This is a very simple dataset, comprised almost entirely of 24-hr, 8kHz, 16-bit audio recordings in .wav format recorded on an acoustic grid in northern Republic of Congo (Figure 1). Collection of these data was made possible through collaboration with the Wildlife Conservation Society<\/a> and the Nouabal\u00e9-Ndoki Foundation, who together administer activities in the national park and surrounds. Funding for this project includes the U.S. Fish and Wildlife Service, the Born Free Foundation, and Lisa Yang.<\/p>\n\n\n\n

The landscape is lowland tropical rainforest comprised of a number of forest types including mixed semi-evergreen forest, monodominant Gilbertiodendron forest, and swamp forest. Fifty acoustic units were placed using a stratified random sampling method on a grid of 25 km2<\/sup> cells.<\/p>\n\n\n\n

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Figure 1. Location of recording grid in Africa (upper left) and location of 50 recording sites in the Nouabal\u00e9-Ndoki National Park and adjacent forestry concession in northern Congo (right). \u2018Mbeli\u2019 and \u2018Goualougo\u2019 are research camps in the national park with permanent staff. \u2018Camp Bonio\u2019 is an old hunting camp in the forestry concession (slightly shaded area south of the river near Mbeli camp and south of acoustic unit 4e). Slightly more than half of the recording sites are in the national park. Coverage Extent:<\/strong> Because of ongoing elephant poaching in this area, exact locations of recording units will be provided only on specific request from researchers. The rectangular extent of the entire grid is latitude 1.89020 to 2.32474, longitude 16.44315 to 16.74016 (WGS84).<\/figcaption><\/figure>\n\n\n\n

License<\/h2>\n\n\n\n

These sound files are freely available for scientific study and exploration, including for the development of detection algorithms. These sounds are not available for any media use including advertising, promotion, radio or movie production, etc. without express permission from the owners.<\/p>\n\n\n\n

Access Help<\/h2>\n\n\n\n

While this dataset is freely available to all users, the wide diversity of ways to interface with the relevant Amazon Web Services S3 bucket means that it is not intuitive to all users exactly how to get to the individual sound files.<\/p>\n\n\n\n

Main registry page for the elephant dataset<\/a>. This link shows the basic information about the dataset and some links to documentation and other information, but the information in the righthand column is not interpretable to many of us.<\/p>\n\n\n\n

The easiest way to access the files is to create a free AWS account. There is no cost to accessing files in the public-access datasets, or to downloading files from these datasets (however using other AWS services might incur charges). This link<\/a> will then take you to the parent directory of the public dataset (to the \u2018Objects\u2019 tab) where you will see a listing of directory entries, including \u2018recordings\/\u2019.  Clicking on this link and then the \u2018wav\/\u2019 link takes you to the listing of each recording site, and within each of these, the actual sound files. (Note that each of these files is about 1.2GB in size, so take a fair amount of time to download and also that there are numerous pages of files within each site folder).<\/p>\n\n\n\n

An alternative is to use the AWS Command Line Interface (CLI), a text-based tool commonly used to perform tasks on AWS, including accessing S3. This does not need an AWS account, provided you use the \u201c\u2013no-sign-request\u201d option in the command line.
For example, to list the contents of all files in the \u2018nn01a\u2019 site folder, you would enter:
aws s3 ls s3:\/\/congo8khz-pnnn\/recordings\/wav\/nn01a\/ \u2013no-sign-request\u00a0<\/strong>
<\/code>(Note that each site folder has hundreds of 24hr sound files to be listed).
To download a single object (sound file) once you know the exact name of the file:
aws s3 ls s3:\/\/congo8khz-pnnn\/recordings\/wav\/nn01a\/nn01a_20201209_000100.wav to\u00a0<\/strong><file path to download location><\/p>\n\n\n\n

Recorder Description and Placement<\/h2>\n\n\n\n