subject

Kadenze: Machine Learning for Musicians and Artists

 with  Rebecca Fiebrink
Class Central Course Rank
#3 in Subjects > Computer Science > Machine Learning

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

Session 1: Introduction 
What is machine learning? And what is it good for?
Session 2: Classification 
This session will cover fundamentals, how to use Wekinator for classification, and an introduction to classification algorithms: kNN, Decision trees, AdaBoost, SVM.
Session 3: Regression 
In this session we will discuss the fundamentals of regression, using Wekinator for regression, and neural networks for more complex types of models.
Session 4: Dynamic Time Warping 
In this session you will learn what dynamic time warping is and what it's useful for, as well as how to use Wekinator for dynamic time warping.
Session 5: Sensors & Features Part I: Basic Signal Processing For Learning 
This session will cover retrieving data from devices: Streaming data vs events; Smoothing noisy signals; Throttling, downsampling, and upsampling; First and second order differences; Buffering & chunking.
Session 6: Sensors & Features Part II: Intro To A Few Fun/Popular Types Of Sensors & Sensing Systems 
This session will introduce Kinect, Leap, and basic physical computing sensors such as accelerometers, gyros, FSRs, ultrasonic distance sensors, and photosensors.
Session 7: Wrap Up 
This session will provide a wrap up for the course, and will discuss practical tools, books, and resources students can access for furthering their work in this field.
8 Student
reviews
Cost Free Online Course
Pace Self Paced
Provider Kadenze
Language English
Certificates Paid Certificate Available
Calendar 7 weeks long
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FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

8 reviews for Kadenze's Machine Learning for Musicians and Artists

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a month ago
Mike O'connor completed this course, spending 12 hours a week on it and found the course difficulty to be medium.
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 f Read More
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. :-)
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a month ago
Alexander Solovets completed this course.
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 Read More
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.
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a month ago
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Anonymous completed this course.
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.
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a month ago
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Anonymous completed this course.
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
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a month ago
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Anonymous completed this course.
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.
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a month ago
Maya Lekova partially completed this course.
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.
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4 weeks ago
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Anonymous partially completed this course.
Really helped to demystify everything about machine learning and AI, no need for any coding or maths expertise either. Would recommend to any musician or artist.
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5 months ago
Mikhail Zyatin partially completed this course.
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