So I’ve not been doing as much music stuff for a while now. I’ve been applying for jobs and it’s been a great opportunity to upskill on machine learning techniques. Obviously really powerful technologies which have loads of exciting use cases, but it can seem a bit daunting to self-learn.
It seems like every start-up position I could find was with companies which wanted to use machine learning in one way or another so this seemed like the right moment to sink my teeth into it. I started with the MIT Intro to Deep Learning Course on YouTube, which is fantastic.
That’s a lie actually, I started with the fantastic FluCoMa package for Max. There are a whole suite of tools in there for using machine learning for music within MaxMSP and if you’re artistically inclined its a really fantastic way to get an intuition for what a lot of the basic machine learning algorithms actually do. I had a lot of fun with that. Here are the courses I followed on Music Hackspace. The instructor, Ted Moore, is great and guides you through a bunch of little projects. I ended up building a classifier tool which translated my beatboxing into drum machine sounds in real time. I’d quite like to use a looper and do a gig with it, but that hasn’t happened yet.
Anyway, I was saying, the MIT Deep Learning course. An excellent primer but I probably got more out of it for having done some hands on practical experiments first in a context I understand well.
Next, I did the IBM “Machine Learning with Python” course on Coursera. Its free, its high quality and gives loads of hands-on examples again. Its more brief than Andrew Ng’s Deep learning specialisation (which I would like to do next, if i get the chance). The IBM course has loads of good visualisations in it and it helped me develop intuition for how some of the algorithms are working at a fundamental level.
Since, then I’ve got myself signed up with Google Colab, which I’d never heard of before, but is fantastic. I absolutely love being able to easily connect to a £20,000 GPU perform training on a model. I’ve been up to all sorts on there and it feels like magic. Downloading and fine-tuning the tiny Gemma2 and Llama3 models from Google and Meta respectively; Getting started with Kaggle competitions; Making my own fine-tuned models and testing out everything I’ve learned about the machine learning toolkit.
Obviously some of these tools have been around for a long time, but they’ve never been so accessible. SciKit Learn is really brilliant and makes coding these applications really simple. Me being me I’ve been looking for ways to encorporate these algorithms into the music creation process. There are a couple of really cool existing tools, like the Synplant-2 VST which uses reinforcement learning to approximate the sounds of samples you put in, using a synth voice. Amazing. I feel like we’re on the cusp of the creative process being total revolutionised and I’m excited for it.
I’m not sure what the right idea for me will be yet. I’m waiting for a technology to come along which opens up a use case which really resonates with me. In the meantime, I have the strong feeling that I’ve found a space that I can work in where something good is going to happen sooner rather than later.