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Deep Learning for Guitar Effect Emulation

teddykoker.com

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Re: Deep Learning for Guitar Effect Emulation

#81

Post by ben7799 »

Scene_Cast2 wrote: post #59
TrackerFF wrote: post #57

Isn't this essentially just learning the case of learning one function, with set parameters? I.e, if you want to build a complete model of the tubescreamer, you'd essentially have to train a model for each possible setting on the pedal - or in other words, every combination of the knobs. Sounds like a real chore, if you were to actually do that physically - and in the end, don't you just want to learn the impulse res…

Not quite. As long as the knobs make consistent changes, just feed some large amount of tests and the model should generalize (smartly interpolate) the rest. What I do have a problem with is that if the pedal is already implemented digitally, then all the human interpretability, along with the classic DSP machinery, is thrown out the window. A better approach would be to build the pedal via a differentiable programmi…

The knobs actually don't behave linearly on a tube screamer. Even the "tone" knob (EQ) doesn't behave at all linearly like you might expect out of consumer audio gear. Tube Screamers have an S-curve potentiometer in use for that knob.

That would be part of the problem with this approach.

Also with this approach you pretty much have to train the model with a near infinite collection of guitars in front of the model and a near infinite number of other effects turned on and off in front of the model.

Re: Deep Learning for Guitar Effect Emulation

#82

Post by abaga129 »

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.

I'm fairly new to the game, but I'm a solo developer. Currently I dont make enough to quit my day job, but it is a nice supplementary income, and it's nice to get paid a bit for something I truly enjoy.

There are also several solo/small shop developers that do make a living from selling plug-ins. Here are a few that I can think of off the top of my head.

Auburn Sounds: https://www.auburnsounds.com/ Valhalla DSP: https://valhalladsp.com/ Kilohearts: https://kilohearts.com/

Re: Deep Learning for Guitar Effect Emulation

#83

Post by cesaref »

Tade0 wrote: post #27
cesaref wrote: post #26

Earlier quoted context omitted.

I think this would be very possible - there was quite a bit of discussion of using NN techniques for modelling fx discussed at DAFx2019 ( http://dafx2019.bcu.ac.uk/ ). There are a number of papers discussing different techniques in the paper archive. Many of the techniques discussed were variations on image processing - transforming the input to the frequency domain then converting this to an image, and applying stan…

Thank you very much. Do you know if there will be a DAFx2020? That would make it the first conference in years that I would really want to attend.

Unfortunately not, it's been delayed. DAFx2020 was due to be in Vienna, and i'm assuming they are still planning on being there, but it's scheduled to be in 2021.

It's a great conference, well worth attending. It's heavy on the maths, but that's DSP for you!

Re: Deep Learning for Guitar Effect Emulation

#84

Post by willis936 »

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…

Effective modeling, yes, but not necessarily accurate modeling. The analog circuits are imperfect in many subtle ways, and component level simulation is rare (if it exists at all, I have not seen it). It’s all a bunch of high level approximations that don’t nail the feel to the point of beating blind tests.

It can be done. I don’t know why we aren’t there.

The audio world is halfway to to the alien truther community: the closer a rational outsider looks at it, the crazier they feel. Technically, it’s a trivial field. Yet here we are with snake oil saturation and subpar solutions.

Re: Deep Learning for Guitar Effect Emulation

#85

Post by fab1an »

abaga129 wrote: post #82
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.

I'm fairly new to the game, but I'm a solo developer. Currently I dont make enough to quit my day job, but it is a nice supplementary income, and it's nice to get paid a bit for something I truly enjoy. There are also several solo/small shop developers that do make a living from selling plug-ins. Here are a few that I can think of off the top of my head. Auburn Sounds: https://www.auburnsounds.com/ Valhalla DSP: http…

what's your link?

Re: Deep Learning for Guitar Effect Emulation

#86

Post by wintermutestwin »

willis936 wrote: post #84
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…

Effective modeling, yes, but not necessarily accurate modeling. The analog circuits are imperfect in many subtle ways, and component level simulation is rare (if it exists at all, I have not seen it). It’s all a bunch of high level approximations that don’t nail the feel to the point of beating blind tests. It can be done. I don’t know why we aren’t there. The audio world is halfway to to the alien truther community:…

https://www.fractalaudio.com/iii/

This guy has been doing component level simulation from the beginning. I have one and it is accurate enough to convince some pretty big players to ditch their tube amps.

Re: Deep Learning for Guitar Effect Emulation

#87

Post by wintermutestwin »

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…

I find that the AxeFX gets enough of the tube amp feel right by modelling amp sag.

It is indeed an amazing time to be a guitar player!

Re: Deep Learning for Guitar Effect Emulation

#88

Post by hashkb »

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…

This is ad hominem. You haven't included any data. You are just as superstitious about your bedroom rig as I am about my basement rig.

Re: Deep Learning for Guitar Effect Emulation

#89

Post by wintermutestwin »

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... ?

As a guitar noob, I'd say that all of those options are great (and yes, I've used them all). If you were more than a noob and had specific needs, I might recommend a specific one to match those needs. I went with the AxeFx because it is insanely tweakable...

Re: Deep Learning for Guitar Effect Emulation

#90

Post by microcolonel »

mochomocha wrote: post #28
microcolonel wrote: post #22

Earlier quoted context omitted.

Not to mention if the inference is done on the CPU, it shouldn't be that hard to control it. The matrices are of a set size by the time you're running a VST; this is the actual simple answer. The medium answer is "this is a wavenet model, so inference is probably really expensive unless the continuous output is a huge improvement to performance".

Indeed. Having myself spent some time in the "VST lifestyle business" when I was in grad school (was selling a guitar emulation based on physical modelling synthesis), and now working in ML, I think there's no chance for such an approach to hit "mainstream" anytime soon. Even if you do your inference on CPU, most deep learning libraries are designed for throughput, not latency. In a VST plugin environment, you're als…

I wonder if teddykoker has looked at applying FFTNet or similar methods as a replacement for Wavenet. I'm not sure but it seems to me like FFTNet is a lot more tractable than Wavenet, and not necessarily that much worse for equivalent training data.
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