Saturday, April 15, 2023

This Boss SD-1 Pedal Does Not Exist


I've been messing around lately with combining Neural Amp Modeler with another open source project I've contributed to in the past - LiveSPICE.

LiveSPICE allows you to simulate audio circuits in real-time, which is very cool. The disadvantage it has is that it is very CPU-heavy. It's CPU usage is also not super consistent, but can be spiky, which makes it hard to use in a live, low-latency environments without getting audio dropouts.

It *is* however, very easy to use it to generate training data for Neural Amp Modeler. So that's what I did.

Because it uses a simulation of the pedal circuit, the generated training audio has none of the added noise that is difficult to fully avoid when capturing actual pedals. This "idealized" version of the pedal should be even easier for the neural net model to learn.

And indeed, it is. Here is the ESR of a "feather" model (the smallest, least CPU-intensive default NAM model type).

It shows 0.000, but it was actually around 0.0001.

You can get the resulting .nam model here on ToneHunt.

Wednesday, March 8, 2023

Neural Amp Modeler (NAM) running on Raspberry Pi

Recently I've been messing around with, and contributing to the Neural Amp Modeler project. It uses machine learning (more specifically the WaveNet model) to create captures of amplifiers and distortion pedals.

It has been gaining a lot of traction recently, with lots of people modeling their equipment. The resulting models are very good.

I've now got it integrated into my Raspberry Pi pedalboard. The audio for the above video was recorded on a Raspberry Pi 4.

The hardware on the pedalboard consists of:

- Raspberry Pi 4
- Hotone Jogg audio interface
- Hotone Ampero Control MIDI Controller
- Wio Terminal (used for a serial-based display)

My pedalboard is using a custom app on top of Jack audio, but I have also made a Neural Amp Modeler LV2 plugin available.