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Real-Time Noise Suppression Using Deep Learning

devblogs.nvidia.com

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Re: Real-Time Noise Suppression Using Deep Learning

#21

Post by Judgmentality »

petra wrote: post #19
acd wrote: post #14

Does this deep learning noise cancelling also work for music with headphones? If so then we can ditch proprietary noise cancelling headphones and just use the phones?

Active Noise cancelling(ANC) headphones are really sensitive to latency. Take An ANC with 0 latency, that stops cancelling noise at 8 khz, and add a 50 usec latency to it, now will stop cancelling noise at ~1.5 khz. But This article talks about 20ms latency.

> Take An ANC with 0 latency, that stops cancelling noise at 8 khz, and add a 50 usec latency to it, now will stop cancelling noise at ~1.5 khz.

How did you calculate this?

Re: Real-Time Noise Suppression Using Deep Learning

#22

Post by hathawsh »

davitb wrote: post #16
konschubert wrote: post #12

I am really impressed with what Nvidia is doing here. I think there is a huge market for improving sound quality in video calls. For me, roughly every second call I make is somehow harmed by some kind of "bad audio" problems. Breathing, reverb, noise, clipping, too silent, there are so many things that can go wrong. And this really harms the productivity of video calls. I have started collecting and building tools to…

Disclosure: I'm the author of the blog post and co-founder at 2Hz. This is a guest post on NVIDIA Developer Blog. The author of the technology is a startup called 2Hz (2hz.ai). Our passion is to improve voice audio quality in audio/video calls. It's a tough problem but also fun to work on. Agree, breathing, reverb, noise are all problems and should be fixed. We started with noise and already shipped a product you can…

Hi! As someone who seems to struggle more than most to understand people on video calls, I'd like to give you my impressions.

Something struck me about the sample video. The very first sample included background noise, but it was very easy to understand regardless of the noise, probably because it was recorded by a pro microphone rather than a phone. Every other sample was far more difficult, regardless of noise removal. Noise removal doesn't really seem to help; in fact, any imperfections in the noise removal process actually make the audio more difficult to understand because I have to guess not only the speaker's voice and the noise but also the algorithm for noise removal.

What does help me is low frequency pickup. I think the first sample is easy because there are plenty of low frequency components that are later lost through the phone.

Low frequencies are presumably difficult to pick up due to the size of the microphone in a phone, but could there be a way to restore those frequencies through audio processing? It would be interesting to analyze the response of specific microphones to specific low frequencies and find patterns that an audio processor could use to restore the low frequency components.

Anyway, kudos for doing some very interesting work. I don't know how representative my experience is.

Re: Real-Time Noise Suppression Using Deep Learning

#23

Post by mehrdadn »

An interesting human problem that I imagine would come up here is that the speaker could be getting distracted with all the noise (crying baby/siren/etc.) while the listener would have no idea what's going on and think the speaker is being confused/dumb/slow/etc... very curious how this would play out in real conversations!

Re: Real-Time Noise Suppression Using Deep Learning

#24

Post by petra »

Judgmentality wrote: post #21
petra wrote: post #19

Earlier quoted context omitted.

Active Noise cancelling(ANC) headphones are really sensitive to latency. Take An ANC with 0 latency, that stops cancelling noise at 8 khz, and add a 50 usec latency to it, now will stop cancelling noise at ~1.5 khz. But This article talks about 20ms latency.

> Take An ANC with 0 latency, that stops cancelling noise at 8 khz, and add a 50 usec latency to it, now will stop cancelling noise at ~1.5 khz. How did you calculate this?

It's an simple explanation of figure 3 here: https://www.edn.com/design/analog/4458544/2/A-perspective-on...

Re: Real-Time Noise Suppression Using Deep Learning

#26

Post by jononor »

ccostes wrote: post #13

Really impressive results, though I wish they had gone more into the deep learning part of it (but I guess that's probably the secret sauce). Can't help but notice how well Nvidia is positioned for what appears to be a growing wave of demand for GPUs. Surprised this hasn't reflected in their share price (feels like they could be the next Intel, but what do it know).

Dedicated chips for machine learning (inference) are being developed by many companies. The hope is that these will be used instead of (or in addition to) GPUs for ML tasks.

Not that Nvidia is poorly positioned. In fact, I expect that if dedicated ML chips work out, Nvidia will also put one on the market.

Re: Real-Time Noise Suppression Using Deep Learning

#28

Post by matrix »

hathawsh wrote: post #22
davitb wrote: post #16

Earlier quoted context omitted.

Disclosure: I'm the author of the blog post and co-founder at 2Hz. This is a guest post on NVIDIA Developer Blog. The author of the technology is a startup called 2Hz (2hz.ai). Our passion is to improve voice audio quality in audio/video calls. It's a tough problem but also fun to work on. Agree, breathing, reverb, noise are all problems and should be fixed. We started with noise and already shipped a product you can…

Hi! As someone who seems to struggle more than most to understand people on video calls, I'd like to give you my impressions. Something struck me about the sample video. The very first sample included background noise, but it was very easy to understand regardless of the noise, probably because it was recorded by a pro microphone rather than a phone. Every other sample was far more difficult, regardless of noise remo…

In my experience it's the loss (or masking) of high frequencies that are the most problematic for understanding speech. The most important sounds in speech are consonants, which are higher frequency sounds. Combine this with foreign accents, and more often than not conference calls quickly degenerate into an unintelligible babble (for me, at least).

Re: Real-Time Noise Suppression Using Deep Learning

#29

Post by davitb »

npunt wrote: post #27

Love it. Don’t really love the idea of audio contents of conversations being routed to a cloud server for processing though — needs to stay on-device for privacy.

This technology is already integrated into Krisp app (https://krisp.ai) and it runs all locally on device.

Re: Real-Time Noise Suppression Using Deep Learning

#30

Post by konschubert »

quadrature wrote: post #17
konschubert wrote: post #12

I am really impressed with what Nvidia is doing here. I think there is a huge market for improving sound quality in video calls. For me, roughly every second call I make is somehow harmed by some kind of "bad audio" problems. Breathing, reverb, noise, clipping, too silent, there are so many things that can go wrong. And this really harms the productivity of video calls. I have started collecting and building tools to…

Some unsolicited feedback. when you list your features could you list the text under the image ?. its a bit tedious to click on each image to get a quick idea about the product. the text is worth a lot more than the picture but is completely hidden.

Hey, yea, that this is a terrible experience currently and I am planning to fix it of course. I'm simply still using the stock images that came with the theme...
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