New AI for sound 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.
AI for sound technology has major potential applications in security, health & wellbeing, environmental sensing, urban living, and the creative sector.
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 issues, 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:
- monitoring of sounds of human activity in the home for assisted living;
- measuring of sounds in non-domestic buildings to improve the office and workplace environment;
- measuring sounds in smart cities to improve the urban environment; and
- developing tools to use sounds to help producers and consumers of broadcast creative content.