Grant winners – 14 January 2016

A round-up of recent recipients of research council cash

January 14, 2016
Grant winners tab on folder

Natural Environment Research Council

Improving high-impact weather forecasts via an international comparison of observation error correlations in data assimilation (OSCA)

UK-Taiwan collaboration on transport and deposition of air pollution over the South China Sea

Spillover of bacteria from agriculture into the surrounding soilscape

Hydroscape: connectivity x stressor interactions in freshwater habitats

Using critical zone science to enhance soil fertility and sustain ecosystem services for peri-urban agriculture in China

Economic and Social Research Council

Measuring information exposure in dynamic and dependent networks (ExpoNet)

Understanding and governing the global business of forced labour

Non-communicable disease epidemiology and public health

Engineering and Physical Sciences Research Council

Research grants

Modelling and inference for massive populations of heterogeneous point processes

Multifunctional gel scaffolds for cell delivery and tissue repair

Additive manufacturing next-generation Supergen energy storage devices

In detail

Award winner: Richard Turner
Institution: University of Cambridge
Value: £565,347

Machine learning for hearing aids: intelligent processing and fitting

There are two big problems that prevent hearing aids being as good as they could be for those who need them. The audio processing strategies employed by the devices are inflexible and do not adapt well to the listening environment, and the hearing tests used for fitting do not allow reliable diagnosis of the underlying nature of the hearing loss, which often results in poor fittings. In applying new machine learning methods to both problems, this project hopes to pave the way for intelligent hearing devices and testing methods that learn about an individual’s hearing loss and thus enable more bespoke fitting. The study will first construct algorithms to facilitate the development of “intelligent audio processing” devices that recognise a user’s acoustic environment. It will then propose a multistage approach to hearing tests in which the model of a patient’s hearing loss is refined after each phase and the results used to design and select stimuli for the next stage.

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