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Deep Learning for Guitar Effect Emulation

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131–140 of 168 posts

Re: Deep Learning for Guitar Effect Emulation

#131

Earlier quoted context omitted.

Sure, but, >Do solo or small shop vst plugin developers make any money?

The implication seems to be "no".

Steve Duda wrote Serum, probably the most popular synth plugin in modern electronic music. everyone I know has a license. so "yes", with the caveat that it's difficult to actually create products of this level of quality

Re: Deep Learning for Guitar Effect Emulation

#132
This isn't bad, but the note decays sound noticeably different. My guess is that the NN doesn't know that human ears have non-linear response that makes them more sensitive to errors in the decay than the attack, so it treats them equivalently. If this is the case then it might be fixable by using logarithmic scale audio samples instead of linear.

The non-linearity of the ear is frequency dependent[0], but in practice I suspect it would be sufficient to pre-process the linear PCM data with x=sqrt(x) and undo before playback with x=x^2.

[0] https://en.wikipedia.org/wiki/Equal-loudness_contour

Re: Deep Learning for Guitar Effect Emulation

#133
post #34

Earlier quoted context omitted.

Yes, it absolutely 100% will, depending on what you mean by handwaving “glitch”. VST is built into chains, and a flaky plugin will derail an entire performance, often making downstream plugins crash. I’m speaking from extensive experience writing plugins and performing with them in multiple hosts and trigger setups. It’s not a robust protocol, but it gets the job done. Are you speaking from some experience with which…

A VST that doesn't fill its buffer on time shouldn't crash another plugin. It's your other plugins that are flaky.

To expand on this, each plugin will receive host managed buffers that they're requested to fill and the input they're expected to process. If they don't do that in time for the host to mix the buffers and deliver the mixed buffer to the audio driver, it simply won't. Nowhere do the plugins directly interact through this process.

If your plugins are crashing because of an underrun you have a much more serious problem than underruns. Then you have plugins writing to or reading from memory that wasn't either handed to them by the host or allocated by themselves. That bad code running in your process can cause it to crash is an orthogonal problem to buffer underruns causing skips or stuttering in audio.

Re: Deep Learning for Guitar Effect Emulation

#134

"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…

In a similar vein, I worked (eg: interned) at a few recording studios in my 20s. Most tracking in both was done to a 2" 24tk analog tape deck and the majority of post and mixing was all done digitally. I don't know what progress has been made in plug-ins in 20 years, I suspect a lot, but at that point there was nothing digitally that came close to the sound of electric guitars overdriven into the tape deck and saturating the tape to an extreme. Now I'm curious if anybody has gotten it right, but there are fewer and fewer studios with 2" tape decks to do a true A/B.

Re: Deep Learning for Guitar Effect Emulation

#135
post #46
post #34

Earlier quoted context omitted.

Yes, it absolutely 100% will, depending on what you mean by handwaving “glitch”. VST is built into chains, and a flaky plugin will derail an entire performance, often making downstream plugins crash. I’m speaking from extensive experience writing plugins and performing with them in multiple hosts and trigger setups. It’s not a robust protocol, but it gets the job done. Are you speaking from some experience with which…

Are there any VST containers? Something that will wrap the VST, intercept under-runs or other bad behaviour and substitute some alternative signal (zero, passthrough, etc.). This could also be part of the host software. The article and your comments inspired in me the idea of a wave-net based VST learning wrapper. If the real plugin fails, substitute a wave-net based simulation of the plugin.

Underruns are not bad behavior. It's the host application's responsibility to hand VSTs buffers to process, and the VSTs themselves have no concept of how much processing time is available to them (except a method that signals to distinguish real-time processing from offline processing) or what it means to underrun the buffer.

The behavior you describe (zero signal on underruns) is a common mitigation. The DAW or the driver itself initializes that'll eventually be handed to the sound card to zero before the host application requests the plugins to process, and if it doesn't have time to mix the plugin outputs it'll play back the initialized buffer instead.

From aea12 one might think that it's normal for an underrun to be fatal. Because underruns are not an exceptional occurrence during production (where you might occasionally load one plugin too many or run a different application with unpredictable load characteristics like a web browser) it really isn't an unexplored area and although they're are a pretty jarring degradation I've never experienced crashes that directly correlated with underruns.

Re: Deep Learning for Guitar Effect Emulation

#136
post #132

This isn't bad, but the note decays sound noticeably different. My guess is that the NN doesn't know that human ears have non-linear response that makes them more sensitive to errors in the decay than the attack, so it treats them equivalently. If this is the case then it might be fixable by using logarithmic scale audio samples instead of linear. The non-linearity of the ear is frequency dependent[0], but in practic…

Why square root and not log?

Re: Deep Learning for Guitar Effect Emulation

#137
post #88

Earlier quoted context omitted.

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.

I guess you are technically right, but this part of the discussion is highly subjective. I was merely pointing out that the quoted statement was subjective and I wasn't using "hipster" as a pejorative - I was actually being somewhat sympathetic to their view. Overall, my goal was to add to this discussion by pointing out the massive progress that has been made and also to show off my supercool signal path in the hope…

It would have gone over better with me (your average analog hipster) if you'd just mentioned the positive aspects of the thing you like. I'm eagerly awaiting the day when modeling is actually good enough for me; and your (common) attitude (that it is, obviously, and anyone who can't hear it is nostalgic/supersitious/hipster) is one of the reasons I don't give modelers a try more often.

Re: Deep Learning for Guitar Effect Emulation

#138
post #132

This isn't bad, but the note decays sound noticeably different. My guess is that the NN doesn't know that human ears have non-linear response that makes them more sensitive to errors in the decay than the attack, so it treats them equivalently. If this is the case then it might be fixable by using logarithmic scale audio samples instead of linear. The non-linearity of the ear is frequency dependent[0], but in practic…

Why square root and not log?

Cheap and dirty fast calculation. I don't actually know what the best mapping is, so I'd start with this.

Re: Deep Learning for Guitar Effect Emulation

#139
post #99
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…

I know of a few shops that took VC money. The big problem isn't the market size so much as how slow the market moves. The product lifetime of a plugin is around a decade. And users hate subscriptions. And it's really hard to determine the value you add to your customers. And no one wants to pay you. It's basically a terrible place to be a developer in it for the money. Really fun work otherwise. The cool gigs are the…

The success of splice would disagree with your notion that “users hate subscriptions”. Given the horrendous price point of many of these plugins it seems to be perfect for a subscription based model. To me it always seemed there is more of a pushback from the industry producing vsts than from the consumers.
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