…at MP3 quality.
This is fine if you like that sort of thing. I would note that latency is very important here: it's not going to be nice to play through if it's incurring any significant latency.
This is fine if you like that sort of thing. I would note that latency is very important here: it's not going to be nice to play through if it's incurring any significant latency.
As the creator of this project I can assure you that the audio used here is at least CD quality (44.1kHz 16bit). With the HiFiBerry hat the digital audio comes in at 24bit/192kHz. The NeuralPi DSP processes the audio at 44.1kHz with 32 bit floating point precision. No reason the sample rate can’t be higher though. Elk OS claims latency is less than 1ms, but I’d like to test and see exactly what the latency is running the plugin. As a guitarist, I can’t tell the difference between this and an analog effect.
runs and ducks for cover
Not necessarily... why? We have been very successful with amp / pedal modeling through regular DSP methods. You'd be very hard pressed to find an album that doesn't use one nowadays. What makes NN methods fundamentally different?
>it's not going to be nice to play through if it's incurring any significant latency.
Yes but this is a non issue for many of the current systems, actually since a couple decades ago. 1-2ms latency is pretty achievable, especially with a RT kernel. That is the natural latency of a sound source about 1 meter away from you.
But it's pretty hard to beat elections flowing through an analog circuit, when in the digital side you have to: convert analog to digital, run through the kernel to get to user space, run the bits through the RNN, send back to kernel space, convert to analog and finally send to an output.
In order to be competitive with a 1980s guitar pedal, you have to do all of that in under ~10ms latency, and that's just really hard still.
Even though we carry around these super computers in our pockets these days, there are still some things left where analog still beats the pants.
There's also another element: if you have, say, a vintage Fender Champ and a Klon (or whatever) it's because you mean to project different expressions through your string handling and note-playing. At that level you've made a best effort to produce the most emotionally transparent and responsive signal chain, which you will then not think about once you've got it turned on and tweaked: ALL the settings are liable to sound 'good' and respond for you.
The modeling approach is so often "This is exactly that, but better, because here are twelve other Fender Champs and models of Klon to choose from!" and when the first claim isn't as true as we would like, and the second is a distraction and time-sink, that's not great.
I can tell when I've chosen wrongly in my music-making tools, because I flat-out stop making music. Even in a dilettantish way: it just stops being a thing. That's a concern.
Things like Clavinova keyboards Piano or Line 6 Pods simulating Guitar/Bass amps & effects have been out their for decades now.
And while they have been quite popular due to the sheer number of sonorities and the convenience they bring (possibility to play with an headset, extremely useful to play at night or in apartments), traditional analog setups remain strong.
Playing on an analog setup still is more pleasant and more expressive IMHO, in particular "simulators" tend to mask the attack when hitting a note, and hides a lot the tension/crispation in the hands/fingers when playing, leading to potential bad habits, specially for people learning to play an instrument. Analog to digital and digital to analog conversion definitely lead to loses in expressiveness.
traditional methods were successful in emulating sounds, but they fail short at replicating the feel and response of the hardware they're trying to replicate
that's why you need neural networks!
> In order to be competitive with a 1980s guitar pedal, you have to do all of that in under ~10ms latency, and that's just really hard still.
Well, don't use a whole PC with software stack. A custom embedded solution with DSP can easily manage.
> Even though we carry around these super computers in our pockets these days, there are still some things left where analog still beats the pants.
Again, it's not digital vs analog, it's massive software stack versus embedded hardware/software solution.
The answer would probably be to reduce the 'learned' output to be a convolution kernel that gets run rather than the RNN itself on the input. Then the kernel only has to change gradually to produce a different sound not continuous processing to produce a particular sound.
Honestly, the only OS that's truly bad at this is Windows. DirectSound is laggy and highly limited in it's capabilities, and even a nice ASIO won't fully alleviate your issues. Your best bet is to get a DAC and hope for the best. Besides that, I've found Linux and MacOS to be very similar in terms of latency, out of the box. However, I've found that tuning Linux with a custom low-latency kernel absolutely destroys CoreAudio's latency. Given that it's something most people won't be doing, I think it's fair to say that both OSes are tied, but I still give the edge to Linux for having a more modular and adaptable sound backend.
This has been solved for over a decade. Linux is a bit tricky but MacOS and Windows have native low latency drivers. There are also a ton of digital effects units running at 0ms on the market.
The only strength of analog processors is that they're dedicated. That an iPhone SoC and run dedicated audio-only OS on it, and there we go.
You can just use a Pi with bare metal code or a real time OS.