Also, the modern TS9 isn't exactly right. I'd love to see this work applied to vintage vs current TS vs modded units.
Deep Learning for Guitar Effect Emulation
21–30 of 168 posts
Re: Deep Learning for Guitar Effect Emulation
#22Earlier quoted context omitted.
Real-time has a few slightly different meanings. So it's hard to say what the author means. One meaning is just that you can guarantee specific deadlines. So if your programme can react within an hour guaranteed, that would be real-time. (Though usually we are talking about tighter deadlines, like what's needed to make ABS brakes work.) For 'real time' music usage you wouldn't need strict guarantees, but something th…
Implementing a VST plugin is literally the exact definition of requiring strict latency guarantees. Your comment winds through a lot of unrelated comparisons to ultimately not make any sense. “Usually fast enough” are three words that guarantee failure in a live show/MIDI environment, which is a large use case of VST and its peers beyond production. By extension, “usually fast enough” further guarantees nobody will e…
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".
Re: Deep Learning for Guitar Effect Emulation
#23End-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 pr…
Even without parameterisation, it might be interesting as a "make my guitar sound like Jimmy Page" kind of tool. Like you said, it will most likely have limitations, but it's still one more tool in the belt, regardless.
That isn't reasonable. There are too many variables beyond the effect, like room, fingers, guitar, and amp. Without the knobs, you haven't delivered the effect.
Re: Deep Learning for Guitar Effect Emulation
#24Excellent 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…
Re: Deep Learning for Guitar Effect Emulation
#25> 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
#26Sounds great and I had to listen to both of the samples to guess correctly. That being said the Tube Screamer is a somewhat simple effect: it's just a distortion with the clipping diodes moved to the feedback loop. How possible would it be to get the famous A/B class amplifier voltage sag and associated changes in parameters of the whole amplifier, or in other words "will it chug"?
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 standard techniques to transform the image, then back to the time domain. There are many compromises with this approach (loosing phase information for example) but with a suitable overlap/add the results were better than I expected, and certainly there's room for further investigation to see if there's useful stuff in there.
Another time domain approach that was applicable to your amplifier model question was an attempt to determine hidden variables in a circuit. Basically, the circuit under test is examined, and rather that build a spice model (which can be laborious) the technique was to expose the interval voltages following components with memory (so capacitors for example). These outputs were included in the NN training model, and so in effect the normally hidden internal state was exposed and allowed for a very good approximation.
Here's the paper:
Re: Deep Learning for Guitar Effect Emulation
#27Sounds great and I had to listen to both of the samples to guess correctly. That being said the Tube Screamer is a somewhat simple effect: it's just a distortion with the clipping diodes moved to the feedback loop. How possible would it be to get the famous A/B class amplifier voltage sag and associated changes in parameters of the whole amplifier, or in other words "will it chug"?
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…
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.
Re: Deep Learning for Guitar Effect Emulation
#28Earlier quoted context omitted.
Implementing a VST plugin is literally the exact definition of requiring strict latency guarantees. Your comment winds through a lot of unrelated comparisons to ultimately not make any sense. “Usually fast enough” are three words that guarantee failure in a live show/MIDI environment, which is a large use case of VST and its peers beyond production. By extension, “usually fast enough” further guarantees nobody will e…
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".
Re: Deep Learning for Guitar Effect Emulation
#29Earlier quoted context omitted.
Real-time has a few slightly different meanings. So it's hard to say what the author means. One meaning is just that you can guarantee specific deadlines. So if your programme can react within an hour guaranteed, that would be real-time. (Though usually we are talking about tighter deadlines, like what's needed to make ABS brakes work.) For 'real time' music usage you wouldn't need strict guarantees, but something th…
Implementing a VST plugin is literally the exact definition of requiring strict latency guarantees. Your comment winds through a lot of unrelated comparisons to ultimately not make any sense. “Usually fast enough” are three words that guarantee failure in a live show/MIDI environment, which is a large use case of VST and its peers beyond production. By extension, “usually fast enough” further guarantees nobody will e…
Re: Deep Learning for Guitar Effect Emulation
#30Pretty 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…