Gemini Listens: Decoding the Soundscape of the Sundarbans

Why Listening Matters for Nature Finance

For conservation and nature finance projects, sound is more than ambience—it is evidence of ecosystem activity, health, and threat.

Decoding bioacoustics enables verification of restoration outcomes and supports trust and transparency in nature-based investments.

Field Audio Collection and Preprocessing

AudioMoth devices were deployed on-site to capture continuous, full-spectrum environmental audio from the mangrove ecosystem.

Collected recordings were standardized using audio preprocessing tools to ensure consistent input quality for downstream AI analysis.

Gemini for Environmental Audio Understanding

Gemini 2.5 Flash was used to directly analyze environmental audio, automatically segmenting recordings and classifying sounds into biophony, geophony, and anthropophony.

The model produces structured, machine-readable outputs with summaries, timestamps, and alerts for potentially harmful sounds.

Species Identification with a Site-Specific Model

When biological sounds are detected, audio is routed to a site-specific BirdNET model fine-tuned for the local species of the Sundarbans.

This localized approach significantly improves accuracy for regional bird populations compared to generic global models.

From Sound to Alerts and Insights

AI-generated summaries convert complex acoustic patterns into clear, human-readable insights.

Threats such as chainsaws or motorized activity trigger immediate alerts, enabling faster response and proactive conservation action.

Conclusion

This pilot demonstrates how modern AI can translate the language of nature into verifiable, financial-grade intelligence. By integrating bioacoustics, large language models, and species recognition, conservation monitoring becomes more transparent, scalable, and impactful.