This is the last of a five-part series on using neural networks for real-time audio.
For the previous article on Stateful LSTMs, click here.
In this article we will go step-by-step to build a functional guitar pedal running neural nets in real-time on the Raspberry Pi.
We have now covered three different neural network models and implemented them in real-time guitar plugins with the JUCE framework. As a guitarist and engineer, the next logical step for me is to build a guitar pedal using what we have learned from the previous articles. …
We will revisit the LSTM for our last neural net model. This time we will use the stateful version and make use of its recurrent internal state to model the Blackstar HT40 guitar amplifier.
For a quick refresher; LSTMs (Long Short-Term Memory) are a type of recurrent neural network commonly used for tasks such as text-to-speech or natural language processing. They have a recurrent state which is updated each time new data is fed through the network. In this way, the LSTM has a memory. …
A few years ago I stumbled on a simple way to de-stress. You can do it anywhere, as long as you have the common office supply known as the post-it note. It’s as simple as it sounds. Just take a few minutes out of your busy day, pick up a pen or pencil, peel off a sticky note, and see what you can do!
I tend to enjoy drawing nature; things like trees, mountains, and landscapes.
This is the third of a fIve-part series on using neural networks for real-time audio. For the previous article on WaveNet, click here.
In this article we will model a guitar amplifier using a Stateless LSTM neural network in real-time.
The LSTM model, which stands for “Long Short-Term Memory”, was developed in the mid 1990s and is a form of Recurrent Neural Network (RNN). Since then, the original model has been modified and applied to many different kinds of problems, including speech recognition and text-to-speech.
Instead of a “feed-forward” neural net like WaveNet, the LSTM has a recurrent state that…
This is the second of a fIve-part series on using neural networks for real-time audio. For the previous Introduction article, click here.
In this article we will model a guitar amplifier using WaveNet in real-time.
WaveNet was developed by the firm DeepMind and presented in the 2016 paper Wavenet: A Generative Model for Raw Audio¹. It explains how the model can be used for generating audio such as realistic human speech. It is a feed-forward neural-net, meaning the information only moves forward through the network, and does not loop as with RNNs (recurrent neural nets).
This is the first of a fIve-part series on using neural networks for real-time audio.
Artificial intelligence impacts our lives more each day, whether we are aware of it or not. From social media feeds to online shopping to self driving cars, A.I. is changing the way we live and how we make decisions.
But wait, isn’t A.I. all about terminators and humanoid robots and machines taking over the world? That’s what we see in movies, but in reality A.I. is just a different way to solve problems. …
What is Guitar Capture/Profiling?
Guitar capture or profiling uses digital technology to capture an aspect of analog hardware. It captures a single “snapshot” of the way an amp/pedal/guitar sounds. This can be anything from EQ to the actual dynamic response of a tube amplifier.
Modelling an electronic circuit is not an easy task, especially when there are non-linear components like vacuum tubes involved. Vacuum tube amps are generally regarded as having superior sound to their transistor based counterparts, and digital modelling. But with recent advancements in digital technology, maybe that perception is changing.
Here is a brief overview of four…
A year ago, I got a fortune in a fortune cookie that said “Do it because you love it.” My first thought was, this is the worst fortune ever. I didn’t think anything else of it at the time, but I did keep the small slip of paper.
I want to start off by saying this is not an article on how to start a successful open source software project. It’s not a technical discussion or an explanation of the technology in my project. …
Engineer, Software developer, Musician, Family man