Membership plan: Getting Started | Topics: Sound Design
Course overview
Markov chains are mathematical models that have existed in various forms since the 19th century, which have been used to aid statistical modelling in many real-world contexts, from economics to cruise control in cars. Composers have also found musical uses for Markov Chains, although the implied mathematical knowledge needed to implement them often appears daunting.
What you'll learn
In this workshop we will demystify the Markov Chain and make use of the popular ml.star library in Max/MSP to implement Markov Chains for musical composition. This will involve preparing and playing MIDI files into the system (as a form of Machine Learning) and capturing the subsequent output as new MIDI files. By the end of the session you will have the knowledge of how to incorporate Markov Chains into your future compositions at various levels.
Who is this course for?
This course is for musicians interested in getting creative with their compositions by using Markov Chains.
Course content
Part 1 - A Non-Musical Probability Example
Part 2 - Building a Basic Markov Chain from Scratch
Part 3 - Implementing ml.markov and Using a Longer Melody to Explore Markov Chain Order
Part 4 - Training on MIDI Data
Part 5 - Increasing the Complexity of the Markov Chain Setup: Velocity
Part 6 - Increasing the Complexity of the Markov Chain Setup: Chords
Part 7 - Introducing the Finished Markov Chain with User Interface
Part 8 - Blending Musical Data from Different MIDI Files
Part 9 - Additional Examples and Blending
Requirements
You should have a basic understanding of the Max workflow and different data types.
Knowledge of MIDI format and routing to DAWs (Ableton, Logic etc) would be a plus, although Max instruments will be provided.
No prior knowledge of advanced mathematical or machine learning concepts are necessary, the focus will be on musical application.
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When you login first time using a Social Login button, we collect your account public profile information shared by Social Login provider, based on your privacy settings. We also get your email address to automatically create an account for you in our website. Once your account is created, you'll be logged-in to this account.