Is hearing-aid signal processing ready for machine learning?
In the hearing-aids community, machine-learning technology enjoys a reputation as a potential performance booster for signal-processing issues such as environmental steering, personalization, algorithm optimization, and speech detection. In particular in the area of in situ hearing aid personalization, the promise is steep but clear success stories are still hard to come by. In this contribution, we analyze the ‘personalizability’ of typical hearing-aid signal-processing circuits. We discuss a few salient properties of a very successful adaptable and personalized signal-processing system, namely the brain, and we discover that among some other issues, the lack of a probabilistic framework for hearing-aid algorithms hinders interaction with machine-learning techniques. Finally, the discussion leads to a set of challenges for the hearing-aid research community in the quest towards in situ personalizable hearing aids.
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