iHearBetter-in-Motion: Unlocking communication in motion for hearing-impaired individuals

Hearing loss is the second most common disability in the UK, affecting 11M people (1 in 6; Action on Hearing Loss, 2015). With an ageing population, by 2035 it is estimated more than 15.6M people in the UK will have hearing loss (1 in 5; Action on Hearing Loss, 2015).

Hearing loops convert sound from electrical sources (e.g., loop system's microphone) into a magnetic signal, which is detected and selectively amplified by hearing aids with hearing-loop settings. Thus, hearing-impaired individuals (HIIs) are presented with the sound-of-interest (e.g., during a concert), without audio interference (feedback or background noise). However, hearing-loop technology cannot be applied in natural conversations and HIIs are left with devices that amplify all surrounding sounds, making them unsuitable for use in environments where more than one person is speaking at the same time in a noisy environment.

Today, while 6.7M people in the UK could benefit from hearing aids, only 2M people use them regularly (Hearing Link, 2019). Multiple studies have found that one of the main reasons for people choosing not to wear hearing aids is their poor performance in noisy situations (McCormack and Fortnum, 2013); "cocktail-party effect".

Consequently, 35%+ of HIIs aged 75+ get through social occasions pretending they can hear things and 25% feel isolated (Royal Voluntary Service, 2018). This is not just an issue in aging communities; over 4.5M HIIs are working age and c75% of these feel their employment opportunities have been limited by their hearing loss (Action on Hearing Loss, 2015). The economic burden of hearing loss to the UK economy has been estimated at £25 billion/year in lost productivity and unemployment (Brunel University London, 2015; Archbold et al., 2014).

With Innovate UK funding (no. 46691), AudioTelligence and the University of Cambridge have developed game-changing assistive listening technology that can separate the voice/sound-of-interest from background noise in real time. Our approach fuses AudioTelligence's patented audio source-separation technologies (WO2019016494) with the University of Cambridge's cutting-edge auditory attention research. By combining audio source-separation algorithms with auditory attention cues, we can automatically select and present the desired audio source to the end user, reducing/eliminating background noise.

However, our existing approach is only applicable to static situations; for example, when HIIs are sat down in meetings or in a restaurant. Here, we will develop new algorithms to extend our approach to encompass situations where HIIs are moving, unlocking communication for HIIs at work and when socialising.