Real-Time Noise Suppression Using Deep Learning • Hacker News Board

Re: Real-Time Noise Suppression Using Deep Learning

#31

Post by konschubert » Wed, Nov 14, 2018, 9:48 PM UTC

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…

Sorry, I noticed that too late. I'm currently reading the paper linked in the blog post. Are there other resources on the topic that you can recommend?

Re: Real-Time Noise Suppression Using Deep Learning

#32

Post by adamloving » Wed, Nov 14, 2018, 10:02 PM UTC

I downloaded the mac app, configured a virtual device to send the system output to the "Krisp Speaker" and verified that it cuts most of the music out of what I'm listening to, leaving only the voice (at a some what degraded quality). I wish I could configure it _cancel_ ambient noise, not just remove it from the input signal.

Re: Real-Time Noise Suppression Using Deep Learning

#33

Post by ghostly_s » Wed, Nov 14, 2018, 10:16 PM UTC

adamloving wrote: post #32

I downloaded the mac app, configured a virtual device to send the system output to the "Krisp Speaker" and verified that it cuts most of the music out of what I'm listening to, leaving only the voice (at a some what degraded quality). I wish I could configure it _cancel_ ambient noise, not just remove it from the input signal.

In your perception, what is the difference between "cancelling" a signal and removing it?

Re: Real-Time Noise Suppression Using Deep Learning

#34

Post by post_break » Wed, Nov 14, 2018, 10:32 PM UTC

My friend is an airline mechanic. One thing his coworkers all had was the jawbone headset. This was back in like 2008-2009. He said he could call up a mechanic working right next to the turbine while it was running and hear him crystal clear. I wonder if any of that technology paired with software technology will make it so there is 0 noise in calls. Maybe an implanted bone mic.

Re: Real-Time Noise Suppression Using Deep Learning

#35

Post by wrycoder » Wed, Nov 14, 2018, 10:41 PM UTC

petra wrote: post #24

Judgmentality wrote: post #21

Earlier quoted context omitted.

> 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...

If I understand that figure correctly, at 8KHz with no latency one gets 12 dB cancellation. With 50 ms latency, 0 dB. And above that frequency, the cancellation actually makes the noise worse. An analog cutoff filter would be needed.

Re: Real-Time Noise Suppression Using Deep Learning

#36

Post by samstave » Wed, Nov 14, 2018, 10:52 PM UTC

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, don't know if you're taking unsolicited requests:

But here are some toggle options I would want a system like this to do (enabled by default):

* Do not send whispers. If I am a primary speaker, and I switch to address someone local to my side of the call via a whisper, that audio should be effectively muted to the other side.

* Focus muting. If I look away from the screen and begin addressing someone off camera, away from the mic, mute that as well.

* Bark and siren filtering. Specifically able to ID and mute barks and sirens. (Planes, motorcycles and trucks would be awesome)

---

What is your imoressikn of the company and app Temi?

Re: Real-Time Noise Suppression Using Deep Learning

#37

Post by def_true_false » Wed, Nov 14, 2018, 10:54 PM UTC

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…

While not nearly as impressive, Chrome's WebRTC AEC stuff (also available via pulseaudio [1]) works pretty well.

[1] - pactl load-module module-echo-cancel aec_method=webrtc

Re: Real-Time Noise Suppression Using Deep Learning

#38

Post by samstave » Wed, Nov 14, 2018, 10:54 PM UTC

mehrdadn wrote: post #23

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!

This is really evident when a speaker hears an echo of themselves, slightly delayed, back through their speakers.

It's really hard to speak when what you say comes echoing back.

Re: Real-Time Noise Suppression Using Deep Learning

#39

Post by nyxtom » Wed, Nov 14, 2018, 10:55 PM UTC

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…

There is a massive market for improving audio quality especially for professional gaming. This would be a huge leap forward.

Re: Real-Time Noise Suppression Using Deep Learning

#40

Post by johnvanommen » Wed, Nov 14, 2018, 10:57 PM UTC

post_break wrote: post #34

My friend is an airline mechanic. One thing his coworkers all had was the jawbone headset. This was back in like 2008-2009. He said he could call up a mechanic working right next to the turbine while it was running and hear him crystal clear. I wonder if any of that technology paired with software technology will make it so there is 0 noise in calls. Maybe an implanted bone mic.

IIRC, that technology originated in fighter jets, and worked it's way down to consumer goods. The company sold Bluetooth headsets for a while, but multi-mic solutions were cheaper and worked better. They tried to hang in there for a few years, diversifying into consumer electronics like the "Jawbox."

It didn't work out, and they went bankrupt.