<story><title>Deep Learning enables hearing aid wearers to pick out a voice in a crowded room</title><url>http://spectrum.ieee.org/consumer-electronics/audiovideo/deep-learning-reinvents-the-hearing-aid</url></story><parent_chain></parent_chain><comment><author>tobtoh</author><text>&amp;gt; The greatest frustration among potential users is that a hearing aid cannot distinguish between, for example, a voice and the sound of a passing car if those sounds occur at the same time. The device cranks up the volume on both, creating an incoherent din.&lt;p&gt;It may be a simplification of the article that I&amp;#x27;m misinterpreting, but as someone who got a hearing aid in early 2016, that&amp;#x27;s not how (modern) hearing aids work.&lt;p&gt;I got my hearing tested which enabled a frequency response of my hearing loss to be plotted (my hearing at low frequencies is fine, at higher freq I have moderate loss). My hearing aid is then tuned to match the inverse of that freq plot (ie boost volume of high frequencies, leave low freq alone).&lt;p&gt;You don&amp;#x27;t actually want a HA that arbitrarily boosts &amp;#x27;speech&amp;#x27; since that won&amp;#x27;t be matched to your needs and has unintended side effects (like music can sound overly harsh&amp;#x2F;bright) because un-needed frequencies are being boosted or supressed).&lt;p&gt;--
On a tangent, after I got my new HAs, I complained to the audiologist that they didn&amp;#x27;t sound very good. Everything sounded far too crisp. She pointed out that having lived with hearing loss for 5-6 years, I actually had almost no idea what something should sound like since my brain had got used to a world with muted high frequency sounds.&lt;p&gt;That blows my mind ... a bit like how do you know the color green is green. Maybe it&amp;#x27;s purple, but you have been told by someone else that it&amp;#x27;s green.&lt;p&gt;After a few weeks, my brain re-learnt what sound should sound like and now it sounds &amp;#x27;normal&amp;#x27; with HA in. Without HA, everything is a little more muffled (as you would expect) and I really notice how much I used to struggle understanding people (I believe my untreated hearing loss contributed to me losing my job a couple of years ago).&lt;p&gt;Hearing aids have changed my quality of life (at age 40).</text></comment>
<story><title>Deep Learning enables hearing aid wearers to pick out a voice in a crowded room</title><url>http://spectrum.ieee.org/consumer-electronics/audiovideo/deep-learning-reinvents-the-hearing-aid</url></story><parent_chain></parent_chain><comment><author>chas</author><text>This approach surprised me. Why are they doing feature extraction and then feeding that into a DNN? It seems much more straightforward to have the input of the network be noisy samples and the output be clean samples a la super resolution[0] in images. They probably wouldn&amp;#x27;t want to use fully-connected layers in that instance, but I don&amp;#x27;t see any fundamental barriers if they have enough computational power to run a neural network already. Am I missing something?&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1603.08155.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1603.08155.pdf&lt;/a&gt;</text></comment>