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Deep Learning for Guitar Effect Emulation

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Re: Deep Learning for Guitar Effect Emulation

#151

Post by williamdclt »

ff7f00 wrote: post #48
whiddershins wrote: post #39

Earlier quoted context omitted.

Do solo or small shop vst plugin developers make any money? I’m curious if anyone has any direct knowledge about that. There are so many professional activities similar to that where no one makes any money and people really just do it for the love, and then there are seemingly similar things like that where people make surprisingly large amounts of money.

There are definitely big players making a lot of money from plugins they develop. Here are a few to check out: ($1200) https://www.native-instruments.com/en/products/komplete/bund... ($300) https://www.soundtoys.com/product/soundtoys-5/ ($500) https://www.arturia.com/products/analog-classics/v-collectio... However, piracy is also pretty big when it comes to plugins.

Having a friend working at Arturia, I don't think they're "small shop" nor do they make "a lot of money" to be honest

Re: Deep Learning for Guitar Effect Emulation

#152

Post by renaudg »

selykg wrote: post #76
wintermutestwin wrote: post #66

"many purists argue that the sound of analog pedals can not be replaced by their digital counterparts." Truly effective modelling of analog pedals, tube amps and guitar cabs has been around for years and is way more cost effective from the bedroom to touring bands. The "purists" are hipsters who value the rarity of some pedals, massive pedalboards and their tube amps. I'm not knocking them - I understand why there is…

I am by no means a musician or an experienced one at that. I tinker and enjoy playing and learning. But I have limited experience overall. My personal experience with electronic tools is the lack of feel. Can I make music with digital tools like AxeFX and similar? Absofreakinglutely. No doubt about it. But those digital tools feel VERY different to me than the real thing. I'm not just talking about a speaker moving a…

>Anyway, anything that gets people playing guitar is, in my opinion, a great thing. We live in a golden age of guitar equipment. I don't think it can honestly get much better than it is right now. It's an amazing time to be a guitar player and incredible options are available at amazing prices.

It sure is a great time for guitar equipment, as the digital revolution has made its way there too.

But being a guitar player is also increasingly lonely : https://www.washingtonpost.com/graphics/2017/lifestyle/the-s...

And it's arguably an opportunity cost for a kid to be pouring so much effort today learning the iconic (but tired) instrument of the boomer generation, when they could be breaking new musical ground instead, mastering Ableton's Push for instance. But to each their own, of course.

Re: Deep Learning for Guitar Effect Emulation

#154

Post by jeremyjh »

whiddershins wrote: post #39
fab1an wrote: post #3

Pretty cool, though I wonder what the latency of this would be if used as a plugin? The author says it works in real-time, but to non music/audio folks this could mean '100 ms latency is real-time enough, right?' Generally, I think the audio VST business is a really fun space to be in for a lifestyle business, as it is way too small to be attractive for VCs. It seems like a space that provides many niches for lots of…

Do solo or small shop vst plugin developers make any money? I’m curious if anyone has any direct knowledge about that. There are so many professional activities similar to that where no one makes any money and people really just do it for the love, and then there are seemingly similar things like that where people make surprisingly large amounts of money.

AFAIK, Mike Schuffham (www.scuffhamamps.com) earns a living developing and selling S-Gear. It might be a semi-retirement or lifestyle type living - not sure - but he's been doing it over a decade now. He doesn't charge as much as he could and gives away free updates for far too long. Despite being a (mostly at least) solo effort, its widely regarded as being a top-tier amp sim. I personally think it sounds better than both Helix and Bias, which are both heavily bank-rolled outfits.

It doesn't have their breadth, but the tones it does have are nearly as good as it gets without serious air movement.

Re: Deep Learning for Guitar Effect Emulation

#155

Post by TrackerFF »

mikevm wrote: post #77
wintermutestwin wrote: post #66

"many purists argue that the sound of analog pedals can not be replaced by their digital counterparts." Truly effective modelling of analog pedals, tube amps and guitar cabs has been around for years and is way more cost effective from the bedroom to touring bands. The "purists" are hipsters who value the rarity of some pedals, massive pedalboards and their tube amps. I'm not knocking them - I understand why there is…

I'm a guitar noob, but have been wanting to pick up an electric for ages :). Quick question - how does that Axe-FX compare to various Amp emulators such as AmpliTube, Line 6 Helix Native, Guitar Rig, Positive Grid BIAS Amp, S-Gear, etc... ?

Axe-FX has good hardware, I've owned every unit since the original Axe-FX came out.

If you want to check this out for yourself, try a Line 6 Helix, and then the Helix Native VST with a normal soundcard. For me, there was orders of magnitude in difference. Good modelling boils down to having excellent hardware.

Re: Deep Learning for Guitar Effect Emulation

#156

Post by fallingfrog »

I think that whereas most guitar effects are really very simple (gain and clipping, or delaying the signal and adding it back in), this approach will probably work just fine.

But, it is sort of using a sledgehammer where a tap from a spoon will do- the original tube screamer is just an op amp and a couple diodes, plus a bit of eq! Not much to it.

Plus, your real problems are going to be noise level (tube screamers in particular are noisy but a discrete transistor distortion can be made very very quiet). your a/d converter, your power requirements (comparable analog distortion effects use a few milliwatts) and cost.

Edit: But that said, this is a super cool project! Good job! Sorry I just realized that what I wrote was kind of negative.

Re: Deep Learning for Guitar Effect Emulation

#157

Post by ssalazar »

melq wrote: post #126
munificent wrote: post #95

Earlier quoted context omitted.

If you drop an audio buffer and fire off a 22kHz impulse into a 50,000 watt soundsystem, you are going to have thousands of very unhappy people and likely some hearing damage.

Point taken, but 22khz is too high for people to hear I think.

Idk exactly what the poster meant but an impulse is broadband, theoretically encompassing all frequencies representable in the host sampling rate.

Re: Deep Learning for Guitar Effect Emulation

#158

Post by ssalazar »

fxtentacle wrote: post #13
svantana wrote: post #5

End-to-end modelling is very enticing for the lazy engineer, unfortunately parameter control (knobs) are an important feature of most audio effects, and sampling enough of the parameter space will become prohibitive for more complex effects. That's why the traditional approach is divide-and-conquer. Also, I don't think this approach won't work well with time-varying effects such as chorus, although I'm happy to be pr…

Sadly, I believe you will be proven correct. What that neural network learns is basically an approximation of a static impulse response. So while it can simulate linear time-invariant effects such as reverb quite nicely, it'll surely have issues with chorus.

> What that neural network learns is basically an approximation of a static impulse response.

Im curious, do you have a reference or source for this? Distortion is non-linear making it impractical to model using an impulse response. Is there something about neural networks that makes them good for modeling non-linear but time-invariant effects?

Re: Deep Learning for Guitar Effect Emulation

#159

Post by mrob »

NobodyNada wrote: post #116
avisser wrote: post #109

Earlier quoted context omitted.

For guitar pedals, there is an implied sub-perceptibility. The output needs to happen as I play - if the delay is too long, it's now a delay pedal. So realtime might match your definition, but it is consistent in audio production. For humans, you can start to notice the lag @ 50ms. (A selection of experimental results summarized here https://gamedev.stackexchange.com/a/74975 )

Latency is much more noticeable when you’re playing a musical instrument; 25-30ms is the point at which it becomes distracting in my (anecdotal) experience as a keyboardist. 50ms would be literally unplayable —- I cannot keep in time if latency is that severe. And that’s total output latency from the moment a key is depressed to the moment the sound comes out the speakers, so it’s important for every component in the…

>50ms would be literally unplayable

You can learn to play it. Pipe organs routinely have more than 50ms latency just from the distance the sound has to travel from the pipes to the organist. Add the time needed to set up steady oscillations in large pipes, and the slow pneumatic actions found in some organs, and >200ms latency is nothing unusual. The important thing is that the latency is consistent.

Re: Deep Learning for Guitar Effect Emulation

#160

Post by ssalazar »

veenkar wrote: post #153

B-but a simple convolution would do the same. Or for faster operation - a transfer function obtained using least squares method. NN is kinda overkill for this, but it's cool POC anyways ;)

Nope- Tubescreamer is non-linear so a simple transfer function won't do it.
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