Details of Award
NERC Reference : NE/Z503617/1
Soprano
Grant Award
- Principal Investigator:
- Mr S R Chapple, University of Edinburgh, Information Services
- Co-Investigator:
- Miss C Lewis, Forest Research, Alice Holt Lodge
- Co-Investigator:
- Dr M Naylor, University of Edinburgh, Sch of Geosciences
- Co-Investigator:
- Dr M Wilkinson, Forest Research, Centre for Sust Mgmt and Climate Change
- Grant held at:
- University of Edinburgh, Information Services
- Science Area:
- None
- Overall Classification:
- Unknown
- ENRIs:
- None
- Science Topics:
- None
- Abstract:
- Imagine yourself sitting in a sun-drenched woodland, eyes closed, on a warm summer day, surrounded by the enchanting symphony of birdsong and the gentle rustling of leaves. This natural auditory experience is a rich source of environmental information, but the challenge lies in translating this wealth of data into a format accessible to environmental scientists and policymakers, as manual data interpretation is not scalable. The solution to this challenge lies in the application of machine learning for sound data classification, a task effectively tackled by our Soprano devices. This technology enables us to convert audio data into manageable datasets, facilitating analysis for environmental practitioners. Consequently, we can transmit concise packages of categorised events, rather than unwieldy volumes of raw audio data, over an LPWAN radio network. A single radio gateway can cover vast areas, spanning tens of square kilometers. Once this telemetry infrastructure is established, it becomes logical to extend its use to transmit other environmental variables such as temperature and water levels. These are well-understood problems and can significantly reduce the costs associated with field data collection. The methods for processing and analysing audible, ultrasonic, and hydrophone data are applicable across the board. Thus, we propose solutions for analysing soundscapes in both terrestrial and aquatic environments. To ensure that this system is not only created but also adopted, we will collaborate with stakeholders and demonstrate its practicality through case studies of interest to organisations such as Forest Research, the Scottish Environment Protection Agency, and the Forth Fisheries Board. The Soprano project will deliver a commercially and operationally ready technology demonstrated on carefully chosen case studies. The Soprano system will provide a standardised and sustainable audio-based EdgeAI platform and include the mechanisms to enable third parties to make remunerated contributions in the development of new EdgeAI capabilities. Increasingly, with the ever growing impacts of climate change becoming more visible, we have seen the burgeoning demand for UK-wide, European and International adoption of automated AI-driven biodiversity and ecology monitoring. Soprano will empower and enable both the wider academic community and entrepreneurs to accelerate the adoption of EdgeAI to address the increasingly urgent environmental challenges we all face.
- NERC Reference:
- NE/Z503617/1
- Grant Stage:
- Awaiting Event/Action
- Scheme:
- Research Grants
- Grant Status:
- Active
- Programme:
- IEM
This grant award has a total value of £441,186
FDAB - Financial Details (Award breakdown by headings)
| Exception - Equipment | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|
| £366,258 | £19,585 | £37,030 | £11,372 | £6,722 | £219 |
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