Machine Learning for Musicians and Artists

Goldsmiths University of London via Kadenze

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Details

Go to class

Provider

Kadenze
Pricing

Free Online Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

50 hours
Sessions

On-Demand
Subtitles

English

A Best Course of All Time

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Have you ever wanted to build a new musical instrument that responded to your gestures by making sound? Or create live visuals to accompany a dancer? Or create an interactive art installation that reacts to the movements or actions of an audience? If so, take this course!

In this course, students will learn fundamental machine learning techniques that can be used to make sense of human gesture, musical audio, and other real-time data. The focus will be on learning about algorithms, software tools, and best practices that can be immediately employed in creating new real-time systems in the arts.

Specific topics of discussion include:

•What is machine learning?

• Common types of machine learning for making sense of human actions and sensor data, with a focus on classification, regression, and segmentation

• The “machine learning pipeline”: understanding how signals, features, algorithms, and models fit together, and how to select and configure each part of this pipeline to get good analysis results

• Off-the-shelf tools for machine learning (e.g., Wekinator, Weka, GestureFollower)

• Feature extraction and analysis techniques that are well-suited for music, dance, gaming, and visual art, especially for human motion analysis and audio analysis

• How to connect your machine learning tools to common digital arts tools such as Max/MSP, PD, ChucK, Processing, Unity 3D, SuperCollider, OpenFrameworks

• Introduction to cheap & easy sensing technologies that can be used as inputs to machine learning systems (e.g., Kinect, computer vision, hardware sensors, gaming controllers)

Syllabus

Introduction

What is machine learning? And what is it good for? We’ll introduce a variety of artistic, musical, and interactive applications in which machine learning can help you create new things.

Classification, Part II; Design Considerations

In this session, we’ll take a deeper look at what it means to build a good classifier, and we’ll explore some common and powerful classification algorithms, including decision trees, Naive Bayes, AdaBoost, and support vector machines. We’ll also dig deeper into an exploration of how learning algorithms can be integrated into your own work most easily to achieve your desired outcomes. You’ll get a chance to explore these new algorithms and continue to work them into your own projects.

Working with Time

In this session, we’ll talk about algorithms that have been specifically designed to help you make sense of changes in data over time. Rebecca will dive into dynamic time warping, and guest lecturer Baptiste Caramiaux will discuss Gesture Variation Follower, an algorithm designed with the arts in mind. You’ll continue to get plenty of opportunities to apply temporal modeling algorithms to real-time data analysis.

Classification, Part I

In this session, we’ll cover the basics of classification, which can be used to make sense of complex data in a meaningful way. We’ll look at two classification algorithms: nearest-neighbor and decision stumps. You’ll be introduced to the Wekinator, a free software tool for using machine learning in real-time applications.

Sensors and Features: Generating Useful Inputs for Machine Learning

Machine learning makes it easier and more fun to work with all sorts of real-time sources of data, including real-time audio, video, game controllers, sensors, and more! We’ll talk about good strategies for making sense of the data you’ll get from different inputs, and for designing feature extractors that make machine learning easier. We’ll be encouraging students to develop their own feature extractors and share them with each other!

Developing a Machine Learning Practice; Wrap-up

Guest lecturer Laetitia Sonami will give a masterclass in which she discusses the way machine learning fits into her own work building new musical instruments, and Rebecca will discuss practical tools, boos, and resources you can access for furthering your work in this field.

Regression

We will discuss the fundamentals of regression, which can be used for creating continuous mapping and controls. We’ll explore the use of linear regression, polynomial regression, and neural networks to create new types of interactions. You’ll gain hands-on practice exploring regression algorithms and starting to apply them to build your own systems.

Taught by

Rebecca Fiebrink

Reviews

4.8 rating, based on 94 Class Central reviews

Start your review of Machine Learning for Musicians and Artists

Mike O'Connor
23

Terrific class for a person looking to bring interactivity to music or visual art. It's also a great introduction to machine learning that goes deep enough to give you an understanding of the tools without taking you ALL the way into a very deep su…

Terrific class for a person looking to bring interactivity to music or visual art. It's also a great introduction to machine learning that goes deep enough to give you an understanding of the tools without taking you ALL the way into a very deep subject.

Rebecca Fiebrink is not only a well-known authority in this field, she also oversees the development of the open source machine-learning tools that you will learn how to use in conjunction with your own art or music.

In my case I learned how to transform a multi-variable stream of electric-power monitoring data into inputs for an Ableton Live composition. I'm planning to add a stream of weather data and another stream of live game-cameras (using photo-recognition) to the mix. This class gave me the confidence to take on that great hack. :-)
Matias Romero Costas @matiasromeroc
2

In the first place, it seemed to me to be a fundamental course because it is a subject that is not very widespread and at the same time very avant-garde. Secondly, despite the complexity of the topics covered, the approach is very simple and makes it possible to approach the topics with very little prior knowledge. Finally, it seems fundamental to me that this theoretical knowledge is supported by free and open access tools. I would also like to make a special mention to Rebecca Fiebrik for her great contribution.
Anonymous

This is not a good class to take. The skills they teach can work in special scenarios, that aren't really used much in the real world. This is not worth hours of your time. Plus, the person who made the course forces you to use their program, so it is basically a 56 hour advertisement.
Anonymous

For me, a dream course which puts together some long standing areas of interest. Pragmatically, this course gives you the tools to introduce meaningful gestural control or input to digital music (my interest) as well as a range of other applications…

For me, a dream course which puts together some long standing areas of interest. Pragmatically, this course gives you the tools to introduce meaningful gestural control or input to digital music (my interest) as well as a range of other applications which emerge in the course and from the forums.
The software tools provided are accessible with good quality free options as well as well-known paid-for options. The teaching material is top-notch and the most exciting part for me is the way that different machine learning approaches are illustrated in a very accessible way, and I have done the straight math versions too ;)
The course-specific Wekinator tool is really useful and lets you hook up whicheve bits of code you can already work with to each other: that bit is pure genius.
Anonymous

I had alot of the suggested equipment so working on this class was straightforward. I appreciate that we focused more on training and use vs writing direct code, while still providing access to the code. It's somewhat of a challenge at first but once you get there it gets fun.
Anonymous

Simply the best and most inspiring introduction to ML that exists out there. Rebecca manages to take creative students all through the landscape, starting from scratch and giving a hands-on experience that enables newbies to experiment creatively from the outset.
I've given the link to several of my students, and I'm happy to say that the course has been a seminal turnaround point for several of them and their later studio practice as graduates.
Alexander Solovets
4

The class is very lightweight, yet gives a solid understanding of how one can apply physic-based models to generate natural looking sound effects. I appreciate that choice of programming language, because the class listeners don't have to waste their time developing building blocks from scratch. I also liked authentic environment used by the lecturer as well as clear and noiseless picture and audio of the lectures. I recommend this class to anyone interested in game development or procedural content generation.

UPD: sorry, this review is for another course from Kadenze.
Anonymous

Great course, very helpful and inspirational. I can recommend this course for anyone wanting to get into machine learning, particularly if you're interested in performance / realtime aspects of the field.
Maya Lekova
3

Great course, interesting tools are used throughout it and the material is presented at just the right level. Didn't have time to finish it.
Anonymous

This course was super inspiring and open minding , as a musician I had so many great things to take from this course, and ever since I took it I try and incorporate Machine learning in my practices. great quality and great lecturer. Highly recommended
Nerea Martinez Bassart

The Machine Learning for Musicians and Artists Course offers incredibly stimulating content while remaining highly accessible for beginners like me who are just stepping into the world of machine learning. The explanations are clear and easy to follow, providing me with a new vocabulary and concepts that now give me the confidence to pursue more advanced training in the field. I particularly appreciated the practical and creative nature of the course—there’s no time wasted, and you quickly dive into hands-on experimentation, seeing results with the software almost immediately. A truly inspiring and empowering learning experience!
Anonymous

This course gives an excellent introduction to machine learning, from an arts perspective. It gives you the ability to explore tools and concepts, hands on, learning by doing. It makes Machine Learning accessible and points the way to possibilities.
Tom Willemse
2

She is good to follow. Her explenations are clear. Easy to understand without any mathematical knowledge. I would like to know a bit more about the codes/ algorithms that are used in this course
Ron Kay
1

Brilliant. I learned a lot and after that course I started to dig a lot deeper into Machine Learning.

For me personally with a background in informatics the first two sessions started a bit slow but at session three it finally got the pace I enjoyed. But given that this course should reach a broad audience this isn't really a negative point.
Anonymous

This course was probably good when it first went up, but at this point many of the software tools no longer work on more recent operating systems. I spent more time modifying software to try and get it to run (with mixed results) than I did working with the AI algorithms. I've also had trouble uploading some assignments.

There is ABSOLUTELY NO SUPPORT. I've filed multiple support tickets with Kadenze and asked for assistance on the forums and have never gotten a reply.

The lectures are very good, but that's it.
Mikhail Zyatin
3
Anonymous

I like this course. What I learned from this course has taught me how to use my computer in a different way. Using instruments to make sound and how to translate to from computer to music is interesting. Want more classes in this manner.
Anonymous

Fantastic course - giving artists and musicians the skills to dig into the variety of powerful machine-leaning techniques. Rebecca Fiebrink is a brilliant teacher, clear and entertaining in complex matters - I told my own students to take this class during summer.. .
Anonymous

I found the course easy to follow and the material relevant to what I wanted to learn. I also liked how easy the software tools were to use. I tried other courses like this one but this was definitely the best.
Anonymous

It is such a timely subject -
Rebecca is a great instructor- Her explanations are clear, lively and accessible.
While the topic is not so easy, her applications and examples help see what can be achieved and thus keep going.