rlichten's music_composition [4 articles]

Abstract

In this paper we introduce a hybrid approach to autonomous music composition by example. Our approach utilizes pattern recognition, Markov chains, and neural networks. We first extract patterns from existing musical training sequences, and then construct a Markov chain based on these patterns with each state corresponding to a pattern. We then use a neural network to learn which shifts of pitch and duration are allowed for each pattern in the training sequences. Using this hybrid model, we compose novel musical ...

Abstract

The ability to construct a musical theory from examples presents a great intellectual challenge that, if successfully met, could foster a range of new creative applications. Inspired by this challenge, we sought to apply machine-learning methods to the problem of musical style modeling. Our work so far has produced examples of musical generation and applications to a computer-aided composition system. Machine learning consists of deriving a mathematical model, such as a set of stochastic rules, from a set of musical examples. ...

Abstract

The goal of this paper is to describe a new approach to algorithmic music composition that uses pattern extraction techniques to find patterns in a set of existing musical sequences, and then to use these patterns to compose music via a Markov chain. The transition probabilities of the Markov chain are learned from the musical sequences from which the patterns were extracted. These transitions determine which of the extracted patterns can follow other patterns. Our pattern matching phase considers three dimensions: ...

Abstract

Several different approaches have been used to explore style oriented music generation. We describe a system where music generating functions are automatically produced with help of genetic algorithms. Genetic algorithms are search and machine learning procedures that are based on natural selection. Genetic algorithms such as any other algorithms can be used to produce musical output. There are few basic approaches when using genetic algorithms: they can be used as an optimising method to solve complex parameterised and/or rule based problems. ...