New version of Wekinator + MOOC "Machine Learning for Musicians & Artists"

[Rebecca Fiebrink]

Hi everyone,

It’s my pleasure to announce that I have released a BRAND NEW version of Wekinator. This is a major new version that includes dynamic time warping alongside new classification and regression algorithms, within an easy-to-use GUI. You can download it here (mac/win/linux) along with many new examples for connecting it to real-time music/animation/gaming/sensing environments: www.wekinator.org

This coincides with today's launch of my online class, Machine Learning for Musicians and Artists: https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info If you’re interested in machine learning for building real-time interactions, please sign up! No prior machine learning knowledge or mathematical background is necessary. The course is probably most interesting for people who can already program in some environment (e.g., Processing, Max/MSP) but should still be accessible to people who don’t. I’m thrilled to be joined by two guest lecturers, music technology researcher Baptiste Caramiaux and composer/instrument builder/performer Laetitia Sonami. You can take the class FOR FREE.

Lecture topics will 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)

Please pass this information on to any friends, colleagues, or students who might be interested.

Best

Rebecca

Rebecca Fiebrink

Lecturer, Department of Computing 

Goldsmiths University of London