This technology is an auditory attention decoding approach that uses a deep neural network to selectively amplify noises of interest in hearing aid technology.
Millions of Americans suffer from hearing loss, and many of these people rely on hearing aids for day-to-day life. Unfortunately, current hearing aids do not work well in noisy environments where there are many competing sounds, as the hearing aid is unable to filter out unnecessary background noise from the acoustic source of interest. As such, a method of separating out extraneous noises is needed to improve hearing aid performance, which in turn will improve communication and mental health in users.
This technology uses machine learning to identify and selectively amplify noises of interest for the user. Incoming sounds in a noisy environment are decoded into individual signals. Real-time brain signals are then coupled with a deep neural network to classify and identify a ‘desired’ sound, without forward or backward prediction. The similarity between incoming sounds and this “desired” sound is computed, then the identified sound of interest is amplified.
IR CU19204
Licensing Contact: Kristin Neuman
