RAPID MIX team member Rebecca Fiebrink has launched a new version of the machine learning software, Wekinator, an incredibly powerful yet user friendly toolkit for bringing expressive gestures into your music, art, making, and interaction design. This is a major new version that includes dynamic time warping alongside new classification and regression algorithms. You can download it here (for mac/windows/linux) along with many new examples for connecting it to real-time music/animation/gaming/sensing environments: www.wekinator.org. The technology that drives Wekinator will feature in many RAPID MIX products that you’ll be hearing about in the future.
The launch coincides the launch of a free online class run by Rebecca, 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, 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. The course features two guest lecturers, music technology researcher Baptiste Caramiaux and composer/instrument builder/performer Laetitia Sonami.
• 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)