Appyling machine learning, engineering professor aims to reinvent the hearing aid
Computer Science and Engineering Professor DeLiang Wang is leading efforts at the Ohio State University to develop technology that helps the hearing-impaired understand speech in the midst of background noise.
My mother’s hardship reflects a classic problem for hearing aid manufacturers. The human auditory system can naturally pick out a voice in a crowded room, but creating a hearing aid that mimics that ability has stumped signal processing specialists, artificial intelligence experts, and audiologists for decades. British cognitive scientist Colin Cherry first dubbed this the “cocktail party problem” in 1953...
It’s time we solve this problem. To produce a better experience for hearing aid wearers, my lab at Ohio State University, in Columbus, recently applied machine learning based on deep neural networks to the task of segregating sounds. We have tested multiple versions of a digital filter that not only amplifies sound but can also isolate speech from background noise and automatically adjust the volumes of each separately.
Prof. Wang also mentions collaborator Starkey Hearing Technologies, a philanthropic supporter of Wang's research.
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. Periodically, hearing aid wearers could update their devices as manufacturers release new versions after retraining the system on new noises. We have filed several patents for the technique and are working with partners to commercialize it, including Starkey Hearing Technologies, in Eden Prairie, Minn., a leading hearing aid manufacturer in the United States.
Read the full article here: http://spectrum.ieee.org/consumer-electronics/audiovideo/deep-learning-….
