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Details of Grant 

EPSRC Reference: EP/L020505/1
Title: Structured machine listening for soundscapes with multiple birds
Principal Investigator: Stowell, Dr DF
Other Investigators:
Researcher Co-Investigators:
Project Partners:
University of Cambridge University of Paris South (Paris XI) Wageningen, University of
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary, University of London
Scheme: EPSRC Fellowship
Starts: 01 May 2014 Ends: 30 April 2019 Value (£): 506,361
EPSRC Research Topic Classifications:
Animal behaviour Artificial Intelligence
Digital Signal Processing Music & Acoustic Technology
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
04 Feb 2014 EPSRC ICT Responsive Mode - Feb 2014 Announced
10 Mar 2014 ICT Fellowship Interviews Mar 2014 Announced
Summary on Grant Application Form
In this Early Career fellowship I will establish a world-leading capability in automatic inference about songbird communications via novel "machine listening" methods, working collaboratively with experts in machine listening but also experts in bird behaviour and communication. Automatic analysis has already shown benefit to researchers in efficiently characterising recorded bird sounds, but there are still many limitations in applicability, such as when many birds sing together. The techniques developed will specifically be designed to handle noisy multi-source audio recordings, and to infer not just the presence of birds but the structure of the signals and the interactions between them. Such methods will be a leap beyond the current state of the art in bioacoustics, allowing researchers to study not just sounds recorded in the lab under controlled conditions, but also field recordings and archive recordings found in public audio archives.

I will develop my techniques through specific application case studies. First through collaboration with David Clayton, an international expert on zebra finch behaviour and genetics, who recently moved his lab to my proposed host institution. The zebra finch is an important "model organism" in biology, because its genome is fully sequenced and it is a useful bird for probing aspects of songbird vocal development. I will collaborate with the Clayton lab to develop methods for automatically inferring the social interactions implicit in audio recordings of zebra finch colonies. Second, I will conduct international research visits to collaborate with other research groups who analyse bird sounds and bird social interactions. Third, I will study the case of automatically detecting bird activity in arbitrary sound archives, such as the soundscape recordings held by the British Library Sound Archive.

Importantly, not only will I apply modern signal processing and machine learning techniques, but I will also develop new techniques inspired by this application area. This fellowship is not about contributing from one field to another, but about building up UK research strength in this cross-discplinary research topic. In order to make the most of this possibility, I will host research workshops and an open data contest to serve as focal points for research attention, and I will also conduct a public engagement initiative to engage the widest possible enthusiasm for this exciting field of possibility.

This fellowship directly aligns with the "Working Together" priority, which is EPSRC's current overriding priority for ICT fellowships.
Key Findings
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Potential use in non-academic contexts
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Summary
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Further Information:  
Organisation Website: http://www.qmul.ac.uk