“Absolutely,” says Oliver Townend, Audiologist and Audiology Communications Manager. “Today’s hearing aids
are already quite sophisticated, and machine learning will add yet another important level of ease and hearing accuracy.”
Townend says machine learning is a natural fit, in particular because of the challenges people with hearing loss face out in real life.
“Hearing in real life constantly changes, because real life itself changes from one moment to the next. And having to think about what hearing aid program to use in a given situation requires cognitive resources. That’s why we’ve already built so much automation into our hearing aids – so people can use their cognitive resources on the task of listening instead of figuring out which program they should use,” Townend explains.
Existing automation systems have challenges
However, he points out, automation is built on assumptions about what to amplify and not to amplify. And, even the best available automated system cannot know what the user intends to hear in any given scenario.
For example, if you’re at a social gathering, do you always want to hear the conversation? Or would you sometimes like to hear more of what that great pianist is playing in the background?
Or think about when you get your hearing aids “fitted” or adjusted at the clinic. You have to explain how you perceive the sound or hearing challenge – which is not always easy. Then, your hearing care professional has to interpret what you’re describing. Things can get lost in translation!
Furthermore, there are thousands of different hearing aid parameters that can be adjusted. If we wanted to compare whether you think “Sound A” is better than “Sound B” on a typical hearing aid, it would take nearly 2,500,000 comparisons to get through all the parameters!
Enter machine learning
In light of the above challenges, Widex suggests a hearing aid solution that is built on machine learning and is driven by the user’s preferences and intentions.
Townend explains: “We propose a simple interface that uses the hearing aid user’s smartphone. Step by step, we can guide the user to better hearing by using simple A/B comparisons. They just choose what sounds best each time. Then, machine learning helps us predict their preferred setting. All you have to do is choose what sounds best, based on what it is you intend to hear. The machine learning algorithm takes care of the rest.”
Widex research has shown that such a system is capable of reaching an ideal hearing aid setting within approximately 20 comparisons, as opposed to nearly 2,500,000.
Maybe your next hearing aid will incorporate machine learning?