Melody Generation in Max – On demand

Level: Intermediate

The importance of the melody in traditional musical composition is difficult to understate. Often one of the first components the ear latches onto, being able to write a good melody is something of an artform. Producing basic algorithmically-generated melodies using Max/MSP is quite easy, but in order to produce something more ‘musical’ we must refine the generation process.

In this workshop you will learn some ways of generating more complex melodies in Max. This will involve implementing occasional phrase repeats to balance predictability and surprise, locking in some of the more important rhythmic elements and incorporating planned octave jumps alongside more restricted pitch-based travel.

By the end of the workshop you will have constructed a melody generation patch that can be set to play along with your compositions, with a greater understanding of some of the ways in which we can sculpt melody in Max.

Topics

    • Max/MSP
    • Algorithmic Composition
    • Melody

Requirements

  • You should be comfortable with the general workflow and data formatting in Max.

  • Knowledge of MIDI format and routing to DAWs (Ableton, Logic etc) would be a plus, although Max instruments will be provided.

  • You should have some basic knowledge of music theory: chords, scales, modes etc.

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.

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.

Algorithmic Composition in Max: Bringing Order to Chaos

Learn to construct music-generating algorithms in Max, to compose semi-autonomously or supplement your compositional practice.

Level: Intermediate 

Composing with randomness

For centuries, musicians have incorporated chance-based elements into their compositions, first through coin flips and dice rolls and more recently through computer software. Today, building music-oriented algorithmic systems is easier than ever with Max.

What you will learn

In this workshop you will learn a variety of algorithmic processes and useful tools to construct your own systems: including drunken walks, list manipulation and step-sequencer pattern generation. Primarily focusing on MIDI-controlled instruments, you will gain an understanding of how chance can be factored into numerous aspects of composition, from melody and harmony to overall piece structure and instrumentation.

By the end of the workshop you will have built a system for algorithmically generating a short multi-instrumental composition which you will be able to go on to improve and expand upon to fit your own preferences.

Requirements

  • You should be comfortable with the general workflow and data formatting in Max.
  • Knowledge of MIDI format and routing to DAWs (Ableton, Logic etc) would be a plus, although Max instruments will be provided.
  • You should have some basic knowledge of music theory: chords, scales, modes etc.

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 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.

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