Using Statistical Models and Evolutionary Algorithms in Algorithmic Music Composition

Using Statistical Models and Evolutionary Algorithms in Algorithmic Music Composition

Ritesh Ajoodha, Richard Klein, Maria Jakovljevic
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-5888-2.ch597
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Background

The Statistical Model

Conklin (2004) reviewed the process of music generation and equated it with the problem of sampling from a statistical model. One can represent a piece of music as a chain of events, which consist of music objects (e.g. notes) together with a duration and an onset time. A statistical model captures the probabilities of different musical features in a piece, given data such the genre and style. For example, in a Rock song, one is likely to see a 4/4 time signature, 5th intervals and it is rare to see notes that aren’t in the current scale. In a Jazz song however, one is likely to see the notes appearing from various modes with a variety of different time-signatures and chromatic runs. To generate music from a statistical model, one samples these different features with frequencies appropriate to the desired style. Conklin (2004) pointed out that statistical models can be beneficial but only a few sampling methods have been explored in the music generation literature.

Key Terms in this Chapter

Genetic Algorithm (GA): A genetic algorithm is a heuristical model of machine learning that is based on the process of natural selection.

Probability of Occurrence (PO): The probability of occurrence is a static constant assigned to every music object in the Statistical Phase to produce a sample.

Statistical Phase: The Statistical Phase is the first phase of a five phased model. The Statistical Phase presents a Context free grammar and statistical model that produces an initial population.

Statistical model: A statistical model is an interpretation that uses variables and equations to show mathematical relationships.

Gaussian Distribution: Gaussian distribution, sometimes referred to as normal distribution, is a mathematical function that defines the probability of a number in some context falling between any two real constants.

Fitness Function: A fitness function is an objective function that is used to evaluate how close a given construction is to achieving the pre-determined criteria.

Context-Free Grammar (CFG): A Context-free grammar is a formal grammar in which every production rule is in the form V ? u, where V is a single non-terminal symbol and u is a string of terminal and/or non-terminal symbols, u can also be empty.

Genetic Phase: The Genetic Phase is the second phase of a five phased model. The Genetic Phase presents a genetic algorithm that refines a statistical sample through a fitness function and genetic operators over a number of generations.

Music Representation: A notational portrayal of acoustic music.

Note Counting: Is a procedure where a scale counter for a corresponding scale counts the occurrences of each note in the sample that belong to the corresponding scale. The corresponding scale with the largest counter value is returned.

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