Deep Learning for Guitar Effect Emulation
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Deep Learning for Guitar Effect Emulation
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Re: Deep Learning for Guitar Effect Emulation
#2That is very cool. Though, part of the pedal are of course the knobs. You'd need to condition the wavenet on the knobs. Did that work well (I assume that you tried that already)?
Also, what is the inference latency on your model? A nice thing about analog guitar effects is that they are blazingly fast.
Re: Deep Learning for Guitar Effect Emulation
#3Pretty 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 small players to thrive in.
As an aside, it's really quite interesting that a lot of cutting edge tech is now used to emulate the hardware-based tech of yesteryear. Think film filters for photoshop, and about 90% of all audio plugins that emulate high end hardware, compressors, pedals, etc etc.
Re: Deep Learning for Guitar Effect Emulation
#4Pretty cool! Is this how Kemper amplifiers work when they do a capture?
Re: Deep Learning for Guitar Effect Emulation
#5End-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 proven wrong.
Re: Deep Learning for Guitar Effect Emulation
#6So this seems similar to an IR (impulse response) where you get a snapshot of an amp mic'd up in a room with knobs fixed at a particular position. In the end, you don't get knobs to fiddle with.
Awesome, I'd love to hear Josh from JHS Pedal's opinion on this.
Re: Deep Learning for Guitar Effect Emulation
#7Pretty cool! Is this how Kemper amplifiers work when they do a capture?
AFAIK Kemper performs multiple passes of impulse-response capture, all at multiple signal levels in order to model non-linearities (like distortion). This is called dynamic convolution. [1] [2]
There are other ways to do that, like Volterra Series, used by Nebula plugins [3]
[1] https://www.uaudio.com/webzine/2004/july/text/content2.html
Re: Deep Learning for Guitar Effect Emulation
#8> We find that the model is able to reproduce a sound nearly indistinguishable from the real analog pedal.
Maybe for the average person or buried in the mix, but the audio samples were easy to distinguish for me as a guitarist. The NN samples unnatural decay were a dead give away.
Re: Deep Learning for Guitar Effect Emulation
#9Excellent writeup, I love seeing real engineering applied to guitar pedals rather than black magic tone chasing.
I'd be really curious to see if the model could be expressed as a transfer function and compared to the schematic for the pedal. The Tubescreamer is a fairly simple circuit but the mystery surrounding it indicates that there are some weird variables at play with the component properties that would lead to additional factors in the transfer function. Wonder if those variables could be identified somehow.
Re: Deep Learning for Guitar Effect Emulation
#10So this seems similar to an IR (impulse response) where you get a snapshot of an amp mic'd up in a room with knobs fixed at a particular position. In the end, you don't get knobs to fiddle with. Awesome, I'd love to hear Josh from JHS Pedal's opinion on this.
This is even more impressive since regular IRs can't duplicate the distortion effect itself, only the frequency response