Earlier quoted context omitted.
Sure, but, >Do solo or small shop vst plugin developers make any money?
The implication seems to be "no".
Deep Learning for Guitar Effect Emulation
131–140 of 168 posts
Re: Deep Learning for Guitar Effect Emulation
#132The 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.
Re: Deep Learning for Guitar Effect Emulation
#133Earlier 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.
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…
Re: Deep Learning for Guitar Effect Emulation
#135Earlier 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.
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
#136This 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…
Re: Deep Learning for Guitar Effect Emulation
#137Earlier 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…
Re: Deep Learning for Guitar Effect Emulation
#138This 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
#139Pretty 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…