MACHINE LEARNING software is available for some brands and models of hearing aids and more great improvements are promising. What is machine Learning?
Real-world applications of machine learning:
- “Google search”
- Recommendation engines on services like “Netflix” and”Spotify”
- “Fastest route” suggestions on “Google Maps”
- Providing ETA estimates on ride-sharing apps like “Uber”
- Providing delivery estimates on food delivery apps like “UberEATS”
- Fraud detection in payment systems (like “PayPal”) and finance
- Medical diagnosis in clinically-complex cases
- Drug prescription assistants
- Identifying tumors and skin cancer
- Spam filtering on popular email clients like “Gmail”
- Speech recognition (“Google, Alexa, Siri”, etc)
- Self-driving cars
- Facial recognition used by “Facebook” and to detect criminals, etc
- Robots that help care for the elderly
- Automatic closed captioning for spoken word and sign language
- Hearing aid performance optimization
Better hearing in background noise
As any experienced hearing aid user knows, hearing in background noise is extremely difficult. Solving the background noise problem is elusive and while there have been a number of technological innovations since the dawn of digital hearing aids (like the directional microphone), only incremental progress has been made in providing a solution.
Over the past few years, DeLiang Wang – a researcher out of Ohio State University – has been working on using machine learning and “deep neural networks” to help make it easier to hear a conversational partner in background noise. Wang’s software enables listeners (with normal hearing and hearing loss) to hear significantly better in background noise.
“People in both groups showed a big improvement (link to complete report) in their ability to comprehend sentences amid noise after the sentences were processed through our program. People with hearing impairment could decipher only 29 percent of words muddled by babble without the program, but they understood 84 percent after the processing”.
Incorporating Wang’s software into a hearing aid would almost certainly revolutionize hearing aid technology, but unfortunately the software is not on the market yet. Based on the following statement, we can safely assume that there will be a significant wait before this technology will be available to consumers.
“Eventually, we believe the program could be trained on powerful computers and embedded directly into a hearing aid, or paired with a smartphone via a wireless link, such as Bluetooth, to feed the processed signal in real time to an earpiece.”
Improved Sound Quality and Greater Listening Comfort
While the solution to background noise is still over the horizon, real progress has been made on improving sound quality and listening comfort through machine learning. The results of a recent double-blind study suggest that machine-learning can assist hearing aid users in more effectively finding their ideal sound settings; sound settings that lead to greater sound quality and listening comfort in a variety of difficult listening settings.
What’s next for machine-learning hearing aids? We don’t know for sure, but we probably won’t see the next real advance for hearing aid users until machine-learning computations can be performed on the hearing aid itself (and that could take a long while). Once hearing aids are capable of super-computer computations – without the assistance of smartphones – we should be getting very close to a real-time speech-in-noise solution; a technology that should help you to finally hear better in background noise.