How does artificial intelligence help recognize animal sounds?
Modern technology takes it a step further: In bioacoustics today, artificial intelligence (AI) aids in the analysis of animal sounds. Why is this important? Imagine weeks of audio recordings from a forest: birdsong, frog calls, and the buzzing of insects. Manually analyzing this data takes a very long time. This is exactly where AI-assisted bioacoustics comes in: algorithms handle a large part of the preliminary acoustic analysis.
Examples of how AI supports bioacoustics:
Automatic analysis of large data sets: AI systems can analyze large amounts of audio recordings much faster than humans. Automatic recognition isn’t perfect, but it significantly reduces the workload. Experts don’t have to listen to every minute in full; instead, they can focus on checking the sections flagged by the AI. This makes it easier to quickly identify which species are present in an area.
Species identification through sound patterns: AI assigns animal sounds to species by recognizing typical sound patterns. To do this, it is trained using many known animal vocalizations. A well-known example is BirdNET. In this process, a call is first converted into a spectrogram—a visual representation of frequency, time, and intensity. The AI then assigns this pattern to a bird species.
Citizen Science and Apps: AI-powered recognition is relevant not only for research teams but also for the general public. Apps like BirdNET or BirdNET Live help identify bird songs in real time. Such apps get many people excited about birds and turn a simple walk into an exciting journey of discovery. This supports citizen science because more people can record species and document observations, even without seeing the bird directly.
Monitoring and Nature Conservation: AI opens up new possibilities in nature conservation projects. Nighttime recordings of migratory birds can be automatically analyzed, making routes and activity patterns more visible. This allows for the development of targeted conservation measures—such as to reduce light pollution, which disorients birds during migration. In general, AI-supported bioacoustics helps monitor endangered species without further disturbing them and identifies trends earlier.
In short: In bioacoustics, AI is a powerful tool for pre-sorting and automatic pattern recognition. The interaction between humans and machines remains crucial: AI provides clues, while experts review the results and interpret them biologically. This makes the acoustic observation of wildlife more efficient and accurate.
In the future, such technologies might even help us gain a deeper understanding of complex animal communication—such as whale songs or primate calls. Exciting, isn’t it?