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Deep Learning for Guitar Effect Emulation

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Re: Deep Learning for Guitar Effect Emulation

#111

Post by samplenoise »

munificent wrote: post #102

I'm not an expert on machine learning or DSP, but I do know just enough of each to suspect this isn't anywhere near as impressive as it seems. A distortion pedal is essentially just a waveshaper [1]. Think of audio in digital terms as just a series of numbers. A waveshaper is just a simple mathematical function. To apply it, you literally just apply the function to each value in the input stream and there's your outp…

> You could accomplish much the same by using a "depth-1 network" and running an impulse through it

This would be true for a linear impulse response, however for this kind of effects you need both state/memory (like a convolution) and non-linearity (like a waveshaper), which is why people use RNN's and CNN's

Re: Deep Learning for Guitar Effect Emulation

#113

Post by Exmoor »

munificent wrote: post #102

I'm not an expert on machine learning or DSP, but I do know just enough of each to suspect this isn't anywhere near as impressive as it seems. A distortion pedal is essentially just a waveshaper [1]. Think of audio in digital terms as just a series of numbers. A waveshaper is just a simple mathematical function. To apply it, you literally just apply the function to each value in the input stream and there's your outp…

Also not an expert, but that sounds about right to me.

I imagine the difficulty in designing these models comes from modeling the variable factors, IE the parameters normally controlled by the knobs on the amp or effect. Some of these should be straightforward (for example "gain" increasing the volume on the input signal), but I suspect that in some pedals these parameters changing can have impacts on how other parameters behave. I don't see any mention of how this "deep learning" model works with that.

Guitar modeling gear has been around for about 25 years (The first Line6 amp debuted in 1996, I'm not sure if their were earlier products brought to market). They've been derided by purists, but have kind of turned a corner in recent years and are now becoming very mainstream.

Some modern products, such as those sold by Kemper, actually allow you to plug in to your existing gear and generate a profile based on the impulse response. The results, at least according to the reviews I've read, are actually very impressive.

Re: Deep Learning for Guitar Effect Emulation

#114

Post by dontreact »

munificent wrote: post #102

I'm not an expert on machine learning or DSP, but I do know just enough of each to suspect this isn't anywhere near as impressive as it seems. A distortion pedal is essentially just a waveshaper [1]. Think of audio in digital terms as just a series of numbers. A waveshaper is just a simple mathematical function. To apply it, you literally just apply the function to each value in the input stream and there's your outp…

I believe you are are vastly oversimplifying this.

An impulse response will characterize only a system that is

* linear

* time-invariant

Many effects are not linear (especially distortion: the crunchiness comes from the nonlinearity). f(a) + f(b) != f(a+b)

And many effects are time varying, for example phasers and choruses which have low frequency oscillators controlling how the sound is shaped depending on when it comes in. Chorus for example will vary the pitch up and down.

Re: Deep Learning for Guitar Effect Emulation

#115

Post by dharma1 »

Here is the original paper from 2019 by Eero-Pekka Damskägg- https://research.aalto.fi/en/publications/realtime-modeling-...

It was also published as a realtime JUCE project, which might be more useful for actual (realtime VST/AU) use:

https://github.com/damskaggep/WaveNetVA

Alec Wright has done more work on this since then, using it for amplifiers:

https://www.aalto.fi/en/news/deep-learning-can-fool-listener...

And time variant effects:

https://github.com/Alec-Wright/NeuralTimeVaryFx

Re: Deep Learning for Guitar Effect Emulation

#116

Post by NobodyNada »

avisser wrote: post #109
jeffbee wrote: post #100

Earlier quoted context omitted.

Is that your private definition of "real-time"? I think it is common to define real-time processing by a specified, finite time between input and output. Many real-time processes are concerned more with the consistency of the latency than with its absolute value.

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 signal chain to have the lowest possible latency. A bunch of 5-10ms delays adds up really quickly.

Re: Deep Learning for Guitar Effect Emulation

#118

Post by TheOtherHobbes »

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…

1ms is usually considered inaudible. 5ms is bearable. 10ms will start to annoy some people. 25ms is actually pretty bad.

Re: Deep Learning for Guitar Effect Emulation

#119

Post by luckydata »

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... ?

AxeFX is the most true to life, the Helix is quite a bit simpler to use, the Kemper has the best "feel" of every simulator. They achieve very similar results sound-wise, all of them can be used in record production no problem.

IMHO for the bedroom player the Helix is the best solution as it's good enough and significantly cheaper than the other options.

Re: Deep Learning for Guitar Effect Emulation

#120

Post by TheOtherHobbes »

boomlinde wrote: post #60
aea12 wrote: post #41

Earlier quoted context omitted.

It is completely irrelevant, given the context. The only, only, only thing real-time means here is “can be run on a live signal passing through it” rather than “is a slow, offline effect for a DAW”. No hard real-time, no soft real-time, no QNX, no pulling out the college compsci textbook. There IS real-time in that sense in DSP, it just isn’t in a VST plugin. I’ll repeat again that any compsci theorycrafting is not t…

Just to be perfectly clear here because I'm not sure you're just using my post as a soapbox or if you have misunderstood my argument: I agree that it's clear what real-time means in this context. I disagree that "usually fast enough" guarantees failure for a VST, because in the case of VST, "usually fast enough" is the only guarantee the host operating system will offer your software. It's not "theorycrafting" to say…

It usually doesn't happen in practice unless you're doing a lot of other things at the same time. Which you shouldn't be.

Of course audio is block buffered over (mostly) USB, and as long as the buffers are being filled more quickly than they're being played out, the odd ms glitch here and there is irrelevant.

As real-time systems Windows, MacOS and Linux are terrible from a theoretical POV, and they're useless for the kinds of process control applications where even a ms of lag can destroy your control model.

But with adequate buffering and conservative loading they work well enough to handle decent amounts of audio synthesis processing without glitching - live, on stage.

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