> The problem with hearing loss is generally not so much that you can't hear anything at all. It's that it becomes extremely difficult to distinguish speech fro

> The problem with hearing loss is generally not so much that you can't hear anything at all. It's that it becomes extremely difficult to distinguish speech from background noise. The problem with hearing loss is that you can't hear. In most cases, there's a loss of high-frequency hearing, you can't hear sibilants and thus can't parse the speech. Speech-in-noise is a specific situation that HAs do not handle well. It's less of an issue with un-aided hearing thanks to the shape of our ears. > The ideal hearing aid will amplify those frequencies at which speech is present, while suppressing frequencies that contain background noise Every modern hearing aid does this already. > a pretty dumb set of heuristics to figure out which is which Dumb? Billions of dollars are waiting for the person who can make noise reduction work really really well. It's surprisingly difficult, even with unlimited computation power. > they'll try to estimate how far the source of the noise is This is 100% fiction. No HA on the planet calculates distance-to-noise. You can spot the HAs that do because they require three microphones not in a straight line. Probably a triangle. You might be thinking of beamforming, where the HA calculates the direction of the sound and can optionally focus amplification on sounds coming from that direction. Typically, sounds behind the listener are amplified less than sounds coming from in front of the listener. This is a useful refinement done by every modern HA. > wind sounds or rustling paper also gets amplified That is unfortunate. There is significant research going into recognising speech patterns so that the HA can make these decisions better. Unfortunately, none have shown useful results yet. > huge opportunity here to apply deep learning To do what, exactly? Why DL? How do you propose to run DL on a 1MHz CPU with 16kb of RAM and a battery the size of a bee's genitals? > It's a challenging hardware problem The hardware has been known and fixed for 20 years. What would you change? Software is where all of the improvements have come from for a very long time. > deep learning part of it has largely been solved Cite me a paper and we can make billions.