Understanding Indian rhythm through simple algorithms – On demand

Level: All Max users

South Indian Carnatic music is home to a huge array of fascinating rhythms, composed from algorithms. Rooted in maths and aesthetics, Carnatic music has many facets that can be applied to computer music. In this workshop you will be given an introduction to this tradition, and provided with the opportunity to observe, create, and hack various patches that demonstrate some of these ideas.

Session Learning Outcomes

By the end of this session a successful student will be able to:

  • Be capable of reciting a simple rhythmic konnakol phrase

  • Be capable of conceiving simple rhythmic algorithms

  • Be capable of translating these concepts into simple Max patches

  • Understand South Indian rhythmic concepts & terminology such as Tala, Jhati, and Nadai

Session Study Topics

  • Learning a konnakol phrase

  • Understanding Tala cycles

  • Understanding Jhati and Nadai

  • Translating rhythmic algorithms into code

Requirements

  • A computer and internet connection

  • A webcam and mic

  • A Zoom account

  • Access to a copy of Max 8 (i.e. trial or full license)

About the workshop leader

Dom Aversano is a Valencian and London based composer and percussionist with a particular interest in combining ideas from the South Indian classical and Western music traditions. He has performed internationally as a percussionist, and produced award-winning installation work that has been exhibited in Canada, Italy, Greece, Australia, and the UK.

For a decade Dom has studied South Indian Carnatic music in London and in Chennai. He has studied with mridangam virtuoso Sri Balachandar, the resident percussionist of The Bhavan music centre in London, as well as shorter periods with Somashekar Jois and M N Hariharan.

An Introduction to Markov Chains: Machine Learning in Max/MSP

Difficulty level: Beginner

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.

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.

Topics

  • Max
  • Markov Chains
  • Machine Learning
  • Algorithmic Composition

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.

About the workshop leader

Samuel Pearce-Davies is a composer, performer, music programmer and Max hacker living in Cornwall, UK.

With a classical music background, it was his introduction to Max/MSP during undergraduate studies at Falmouth University that sparked Sam’s passion for music programming and algorithmic composition.

Going on to complete a Research Masters in computer music, Sam is now studying a PhD at Plymouth University in music-focused AI.