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Machine Learning for Musicians and Artists (Kadenze)

Machine Learning for Musicians and Artists (Kadenze)

Key Facts

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By enrolling in this online course you will spend approx. 7 Sessions / 8 hours of work per session to learn key concept of Machine Learning.

Course Overview

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)

Course 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.

Meet your Instructors

Rebecca Fiebrink

Dr. Rebecca Fiebrink is a Lecturer in Computing at Goldsmiths, University of London. She creates new technologies for digital music and art, and she designs new ways for humans to interact with computers in creative practice. Much of her current research combines techniques from human-computer interaction, machine learning, and signal processing to allow people to apply machine learning more effectively to new problems, such as the design of new digital musical instruments and gestural interfaces for gaming and health. She is also involved in projects developing rich interactive technologies for digital humanities scholarship, and in designing new approaches to integrating the arts into computer science teaching and outreach. Rebecca is the developer of the Wekinator system for interactive machine learning. She has worked with companies including Microsoft Research, Sun Microsystems Research Labs, Imagine Research (recently acquired by iZotope), and Smule, where she helped to build the #1 iTunes app “I am T-Pain.” An active musician, she has performed regularly with a variety of musical ensembles, including as a laptopist in Sideband, the principal flutist in the Timmins Symphony Orchestra, and the keyboardist in the University of Washington computer science rock band “The Parody Bits.” Prior to arriving at Goldsmiths, she held a faculty position at Princeton University.

* We try to provide you as many free online courses from multiple providers and universities but in some cases you may have pay nominal fee to get assignments and/or to claim verified Certificate or Degree from respective provider.

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Machine Learning for Musicians and Artists (Kadenze)


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