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Musicology in a virtual world: a bottom up approach to the study of

Bosma, M. (2005) Musicology in a virtual world: a bottom up approach to the study of. Doctoral, Artificial Intelligence.

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Abstract

The objective of this project is to implement and study a model inspired by the mimetic agents of Miranda [16], who proposes a society of musical autonomous agents that evolves a common repertoire of intonations from scratch by interacting with one another. These agents are furnished with a vocal synthesizer, a hearing apparatus and a simple brain. They communicate by playing imitation games [2]. The model studied in this project adds to the agent architecture a neural network brain, a simple grammar learning device, realistic ears, and a music culture. Realistic ears is a metaphor for a pitch detection algorithm that can infer a wrong pitch, depending on the timbre of the instrument. The SARDNet [10] is the neural network brain of choice since it is able to handle sequences. The fact that music is ordered in time is a very important characteristic which is preserved by using a SARDNet. In this project we found that the SARDNet-brains of the agents learned human-made songs easier than randomly generated songs. It turned out that these human-made songs, which were successful in our world, were successful in the agent world as well. The success was measured as learnability. A second part of the agent brain is a grid of probability tables. This grid can be seen as a grammar learning device. It learns the transitions between elements of a melody. The music knowledge of the agents does not start from scratch, but instead we assume that the culture of the agents has already a history. This cultural history is modeled as a set of songs. Some of these songs are real compositions from our world, like Invention Number 1 of Bach, while other songs are randomly generated melodies produced by the computer. A song of this set can be linked to an agent of the society. This agent is said to study" its culture. It means that the neural network of an agent is trained on the material of one of the songs. An agent is able to compose music based on its knowledge which is stored in the neural network brain. To evaluate these compositions, I have developed a classifying tool that compares agentcompositions to the set of songs. During learning, the compositions of an agent became less similar to the song it was assigned to, but they generally could be classied as belonging to the assigned song. Three out of four agents made compositions that were more similar to the songs they studied, than to the other human-made songs present in the set.

Item Type: Thesis (Doctoral)
Degree programme: Artificial Intelligence
Thesis type: Doctoral
Language: English
Date Deposited: 15 Feb 2018 07:28
Last Modified: 15 Feb 2018 07:28
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/8462

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