About This Project

The project primarily focuses on identifying specific acoustic signatures that trigger responses from congeneric but phylogenetically distant babbler species. Secondly, it utilizes CNN-based AI models to detect species that form associations in the acoustic space during dawn chorus, serving as a captivating manifestation of intraspecies communication within complex tropical environments while emphasizing the broader context of whole soundscapes to assess biodiversity.

Ask the Scientists

What is the context of this research?

Studies have shown that birds use the information encoded in alarm signals of other species in mixed-species flocks. Social learning through a process of acoustic association can help them in recognizing unknown alarm calls of heterospecific species and increase their survival chances against predators. But social species of babblers like Jungle Babbler and Large Gray Babbler respond even to non-alarm signals of one another. Why do they recognize these calls? They aren't even closely related unlike these wren species. The second part of this project aims to look at soundscapes as a whole to understand patterns of diversity during dawn chorus which seems to be getting quieter and less diverse.

What is the significance of this project?

There should be shared acoustic signatures that foster responses between distantly related babblers in both shared & separate environments and the discovery of those challenges our understanding of interspecies communication, offering new perspectives on the evolution of vocalization in birds. If animals from different species use similar signals to convey information, they might have evolved similar ways of communicating with each other. This convergence might be driven by shared ecological factors, revealing ways in which diverse species adapt to their environments. Furthermore, analyzing soundscapes can act as a valuable proxy for assessing biodiversity. We have observed that they vary with the seasons and across different locations, impacting dawn chorus diversity.

What are the goals of the project?

For my primary objective, vocalizations of both species are collected & multiple acoustic features are extracted from the generated spectrograms. Deep learning models are trained on these features to build a classifier. Model's performance is analyzed to understand if it effectively distinguishes between the vocalizations of two species. Misclassifications might suggest sharing of acoustic features. Secondly, I am collecting dawn chorus recordings. A species detection algorithm based on CNN/RCNN, will be employed to identify avian species vocalizing in the same acoustic space & time as manual annotations are time consuming. Next, we look for correlations between soundscape variations & acoustic space use. This will help us in looking at how dawn chorus is changing spatially & temporally.