Designing better hearing aids using physiologically inspired speech enhancement

Hearing aids can profoundly transform the lives of people with hearing impairments but in the UK alone about 6 million such people do not use them. An important reason for this is that conventional hearing aids don't make it easy to distinguish meaningful signals such as speech from background noise. The combination of a fully functioning ear and the brain are fantastically good at this job and easily outperform the best computerized speech-recognition apps when even just a small amount of noise is present. A conventional hearing aid amplifies both the speech and noise indiscriminately, so even though the neural pathways of the brain may be unimpaired the task of distinguishing speech from noise becomes much harder. Various approaches to automatic speech enhancement have been tried but none comes close to what nature can do.
Despite the demand for better solutions, recent progress in research and development has been slow and no breakthrough technology has yet emerged. Speech enhancement strategies have been generally developed on the basis of mathematical, but not physiological principles. These methods, although based on fundamentally different strategies, have two things in common: first, they operate on signal features that rely primarily on the signals' local energy, and second, they have not improved speech intelligibility. Here, we propose overcoming this conceptual barrier by developing engineering solutions to the speech-in-noise problem that are based on physiological principles. The same technology will also be of benefit for automatic speech recognition systems, since the problems of both are similar.
We expect to see direct applications of our work to be implemented in hearing aids within the next 5 years. Two of the biggest companies in the world in their field (Siemens for hearing aids and Google for signal processing) demonstrate the interest, support and the confidence toward this approach. We have substantial experience with the whole development cycle: we have invented, designed, evaluated and implemented a noise reduction scheme that will be part of the next generation of Cochlea Ltd. cochlear implants.
Our central hypothesis in this proposal is that the brain uses sparse coding when distinguishing meaningful signals from noise and it uses a dynamic dictionary for sound representation. We are going to investigate this coding mechanism in individual neurons in the auditory brainstem, and based on the results, will develop novel signal-processing strategies. We expect that these algorithms will be better than conventional algorithms and consequently can help hearing impaired. An animal model is essential to this project, because it is impossible to study responses of individual neurons from the auditory brainstem in humans. Sparse
Neuron adapt their response because they have a limited dynamic rate which they constantly optimize in response to the environment in order to reduce redundancy and to maximise the information flow. In this project, we are going to extend the description of static neural response patterns to include a time varying and context sensitive components and we will measure these dynamic responses in single neurons in the brain stem. Knowing how neuronal responses change in noise will enable us to create a dynamic dictionary that can be used for sparsification. We expect that in this representation speech and noise is separable.
We will use these dynamic response pattern as the basis of a novel transformation and sparsification in order to enhance the components of speech that are relevant for understanding, thus improving speech intelligibility without reducing the quality.
We will evaluate the algorithm in substantial clinical trials.

The total annual cost of hearing impairment in the EU has been estimated to be Euro224 Billion (EuroQol: Euro16K/QALY). In the UK alone 6 million more people could benefit from a hearing aid (HA) (£18 Billion p.a.). These costs are mainly a consequence of economical inactivity due to hearing loss.
We have designed a programme that will lead to better hearing aids. We present a programme that integrates physiological recordings, signal processing and evaluation in a coherent work plan. Letters from two of the biggest companies in the world in their field (Siemens for hearing aids and Google for signal processing) demonstrate the interest and support that the world leading industry has toward this approach. We therefore expect to see direct applications of our work to be implemented in hearing aids within the next 5 years.
Results of this research are designed to break this deadlock and create the impulse for a step change in the signal processing research. Application of the results of this research can therefore lead to improved signal processing algorithms that will enable hearing aid users to better communicate. The results of this project can directly improve the quality of life for 250 million people around the world.
Hearing aids are a market worth around billions world-wide and progress in signal processing is closely watched by the hearing aid industry. Siemens, one of the biggest hearing aid company in the world has expressed the confidence in our leadership and their interest in this work. Siemens, as well as other hearing aid companies have a direct interest to improve hearing aids to remain competitive in the market and they have a track record of quickly implementing novel signal processing strategies. We are aware of IP-issues, and we have established good communication channels to avoid conflicts in future. It is planned to patent results as soon as possible. IP will remain with the University of Southampton and the University of Cambridge.
The provision of hearing aids is a big part of the NHS budget and attempts are made in various ways to reduce costs. The policy of central tendering by the NHS supply chain enables the cost effective provision of huge numbers of hearing aids, however, a big burden on the cost in this package is that large numbers of aids are not used by wearers after disappointing results soon after consultation and allocation. There is therefore a huge potential for higher efficiency by improving the quality of aids with the side effect of increased hearer satisfaction and time of wearing.
The programme is aligning technical developments with matching experimentation. These experiments will result in useful data for the development for novel signal processing algorithms and can be used in many labs all over the world. The following research streams are also interesting for a variety of researchers:
- The sparse coding algorithm is interesting for several interest groups. It can be used in hearing aids, but also in cochlear implants, signal compression schemes and communication devices like phones. Google, one of the biggest companies in the world describes in their letter of support how important new research approaches in this area are.
- The neural data collected will be extremely interesting to neuroscientists interested in sensory neural coding and these experiments will be closely scrutinized by the neuroscience community worldwide.
This programme is of an essential interdisciplinary nature. The success depends crucially on the demonstrated quality and variety of the background of its members. The success of the programme will be measurable by the number of publications in peer reviewed journals and conference presentations of results in national and international conferences.