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Details of Grant 

EPSRC Reference: EP/T019751/1
Title: AI for Sound
Principal Investigator: Plumbley, Professor M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Accenture Global Solutions Limited Audio Analytic Ltd (UK) BBC
Connected Places Catapult Digital Catapult Environment Agency (Grouped)
Pindrop (UK) Samsung The Alan Turing Institute
UK Dementia Research Institute
Department: Vision Speech and Signal Proc CVSSP
Organisation: University of Surrey
Scheme: EPSRC Fellowship
Starts: 01 May 2020 Ends: 30 April 2025 Value (£): 2,120,276
EPSRC Research Topic Classifications:
Artificial Intelligence Music & Acoustic Technology
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Jan 2020 EPSRC ICT Prioritisation Panel January 2020 Announced
25 Feb 2020 ICT Fellowship Interviews 25 February 2020 Announced
Summary on Grant Application Form
Imagine you are standing on a street corner in a city. Close your eyes: what do you hear? Perhaps some cars and busses driving on the road, footsteps of people on the pavement, beeps from a pedestrian crossing, rustling and clonks from shopping bags and boxes, and the hubbub of talking shoppers. You can do the same in a kitchen as someone is making breakfast, or as you are working in a busy office. Now, following the successful application of AI and machine learning technologies to the recognition of speech and images, we are beginning to build computer systems to tackle the challenging task of "machine listening", to build computer systems to automatically analyse and recognize everyday real-world sound scenes and events.

This new technology has major potential applications in security, health & wellbeing, environmental sensing, urban living, and the creative sector. Analysis of sounds in the home offers the potential to improve comfort, security, and healthcare services to inhabitants. In environmental sound sensing, analysis of urban sounds offers the potential to monitor and improve soundscapes experienced for people in towns and cities. In the creative sector, analysis of sounds also offers the potential to make better use of archives in museums and libraries, and production processes for broadcasters, programme makers, or games designers. The international market for sound recognition technology has been forecast to be worth around £1bn by 2021, so there is significant potential for new tools in "AI for sound" to have a major benefit for the economy and society.

Nevertheless, realising the potential of computational analysis of sounds presents particular challenges for machine learning technologies. For example, current research use cases are often unrealistic; modern AI methods, such as deep learning, can produce promising results, but are still poorly understood; and current datasets may have unreliable or missing labels.

To tackle these and other key isues, this Fellowship will use a set of application sector use cases, spanning sound sensing in the home, in the workplace and in the outdoor environment, to drive advances in core machine learning research.

Specifically, the Fellowship will focus on four main application use cases: (i) monitoring of sounds of human activity in the home for assisted living; (ii) measuring of sounds in non-domestic buildings to improve the office and workplace environment; (iii) measuring sounds in smart cities to improve the urban environment; and (iv) developing tools to use sounds to help producers and consumers of broadcast creative content.

Through this Fellowship, we aim to deliver a step-change in research in this area, bringing "AI for Sound" technology out of the lab, helping to realize its potential to benefit society and the economy.
Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.surrey.ac.uk