Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
1–10 of 60 posts
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#2Tech starts 1/3 the way down the article (ctrl+f "clean speech")
> My lab was the first, in 2001, to design such a filter, which labels sound streams as dominated by either speech or noise. With this filter, we would later develop a machine-learning program that separates speech from other sounds based on a few distinguishing features, such as amplitude (loudness), harmonic structure (the particular arrangement of tones), and onset (when a particular sound begins relative to others).
> Next, we trained the deep neural network to use these 85 attributes to distinguish speech from noise.
> One important refinement along the way was to build a second deep neural network that would be fed by the first one and fine-tune its results. While that first network had focused on labeling attributes within each individual time-frequency unit, the second network would examine the attributes of several units near a particular one
> Even people with normal hearing were able to better understand noisy sentences, which means our program could someday help far more people than we originally anticipated
> There are, of course, limits to the program’s abilities. For example, in our samples, the type of noise that obscured speech was still quite similar to the type of noise the program had been trained to classify. To function in real life, a program will need to quickly learn to filter out many types of noise, including types different from the ones it has already encountered
oh
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#3Tech starts 1/3 the way down the article (ctrl+f "clean speech") > My lab was the first, in 2001, to design such a filter, which labels sound streams as dominated by either speech or noise. With this filter, we would later develop a machine-learning program that separates speech from other sounds based on a few distinguishing features, such as amplitude (loudness), harmonic structure (the particular arrangement of to…
That last part seems to really put a damper on things, since the problem the author describes is that a person with a hearing aid requires speakers to take turns. Apparently, when people speak together, the multiple voices clash. Even if the hearing aid amplifies voices only, that problem remains.
Still, cooler than a lot of things Deep Learning is being applied to these days.
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#4Tech starts 1/3 the way down the article (ctrl+f "clean speech") > My lab was the first, in 2001, to design such a filter, which labels sound streams as dominated by either speech or noise. With this filter, we would later develop a machine-learning program that separates speech from other sounds based on a few distinguishing features, such as amplitude (loudness), harmonic structure (the particular arrangement of to…
As someone with a cochlear implant who lives with the consequences of overly clever programmers who thought they'd "help" by filtering out noise and volume and whatever else... I really wish they wouldn't. This is a technology that makes me so angry some days that I sometimes wonder if it was worth getting implanted, even though I know it was.
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#5Tech starts 1/3 the way down the article (ctrl+f "clean speech") > My lab was the first, in 2001, to design such a filter, which labels sound streams as dominated by either speech or noise. With this filter, we would later develop a machine-learning program that separates speech from other sounds based on a few distinguishing features, such as amplitude (loudness), harmonic structure (the particular arrangement of to…
As someone with a cochlear implant who lives with the consequences of overly clever programmers who thought they'd "help" by filtering out noise and volume and whatever else... I really wish they wouldn't. This is a technology that makes me so angry some days that I sometimes wonder if it was worth getting implanted, even though I know it was.
Which one have you got? I know what you mean with the "helpful" bullshit. My conventional hearing aid, on my left ear, has this "smart" mode where it tries to detect speech vs noise, and change the volume or the directionality of the microphone to compensate. You end up with this wildly fluctuating volume all the time where it feels like stationary objects are coming at you. I had them turn that feature off asap.
On the other hand, my cochlear implant (right side), has a directional microphone that's actually incredibly useful in noisy situations. Combined with the directional mic on the hearing aid, I can actually hear almost as well as a normal person in a crowded bar, after 20+ years of avoiding them because of how impossible they were to cope with.
I strongly recommend it if you can get it - the Nucleus Freedom 6. I'm saving my pennies up to get a second one.
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#6> 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.
It may be a simplification of the article that I'm misinterpreting, but as someone who got a hearing aid in early 2016, that's not how (modern) hearing aids work.
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).
You don't actually want a HA that arbitrarily boosts 'speech' since that won't be matched to your needs and has unintended side effects (like music can sound overly harsh/bright) because un-needed frequencies are being boosted or supressed).
-- On a tangent, after I got my new HAs, I complained to the audiologist that they didn'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.
That blows my mind ... a bit like how do you know the color green is green. Maybe it's purple, but you have been told by someone else that it's green.
After a few weeks, my brain re-learnt what sound should sound like and now it sounds 'normal' 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).
Hearing aids have changed my quality of life (at age 40).
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#7This 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't want to use fully-connected layers in that instance, but I don't see any fundamental barriers if they have enough computational power to run a neural network already. Am I missing something?
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#8This sounds like something that could have a potential use for non-hearing-impaired people who have sensory overload issues (e.g. autism).
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#9> 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. It may be a simplification of the article that I'm misinterpreting, but as someone who got a hearing aid in early 2016, that's not how (modern) hearing aids wor…
I probably need a hearing aid but I am very reluctant to go for a testing because the last time I had one (very long ago and very far away from Canada, I admit) I thought it was incredibly imprecise: they asked me to press a button when I could hear a sound and I was absolutely unsure whether I heard really something already or just imagined. Is it still so very subjective?
Re: Deep Learning enables hearing aid wearers to pick out a voice in a crowded room
#10> 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. It may be a simplification of the article that I'm misinterpreting, but as someone who got a hearing aid in early 2016, that's not how (modern) hearing aids wor…
I probably need a hearing aid but I am very reluctant to go for a testing because the last time I had one (very long ago and very far away from Canada, I admit) I thought it was incredibly imprecise: they asked me to press a button when I could hear a sound and I was absolutely unsure whether I heard really something already or just imagined. Is it still so very subjective?
Yup - it's still done by that method. Although they do run through multiple frequencies multiple times to try and get consistency.
I know what you mean though ... especially if you suffer a bit from tinnitus, it's sometimes really difficult to distinguish if you are hearing a high freq test tone, or just the tinnitus 'noise'.