AI identifies jungle noises

A recent study has shown that the use of artificial intelligence (AI) in analyzing soundscapes can accurately identify different bird species in various stages of forest development. By simply allowing AI to listen, one can easily determine the health of the rainforest.

Through reliable measurements, the AI technology was able to assess how Ecuadorian tropical forests were naturally regenerating after agricultural use of the lands ceased. By recognizing the sounds of local bird species, the AI could accurately determine the stages of species composition, even though it could only classify a fraction of the species identified by experts.

The forests studied ranged from pasture land and cocoa plantations to forests of different ages growing in their place, ultimately leading to old primeval forests. The assessment made by the artificial intelligence aligned with that of experts who listened to the sounds of birds, amphibians, and mammals, as well as the analysis of nocturnal insect species.

Researchers from Germany, Ecuador, and America suggest that using listening artificial intelligence could be a cost-effective method to monitor the regeneration of tropical forests globally. This approach could ensure the creation of not only carbon sinks but also biodiversity-rich environments in various parts of the world.

By Samantha Johnson

As a content writer at, I craft engaging and informative articles that aim to captivate readers and provide them with valuable insights. With a background in journalism and a passion for storytelling, I thoroughly enjoy delving into diverse topics, conducting research, and producing compelling content that resonates with our audience. From breaking news pieces to in-depth features, I strive to deliver content that is both accurate and engaging, constantly seeking to bring fresh perspectives to our readers. Collaborating with a talented team of editors and journalists, I am committed to maintaining the high standards of journalism upheld by our publication.

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