Neat drummer : computer-generated drum tracks

Abstract

Computer-generated music composition programs have yet to produce creative, natural sounding music. To date, most approaches constrain the search space heuristically while ignoring the inherent structure of music over time. To address this problem, this thesis introduces NEAT Drummer, which evolves a special kind of artificial neural network (ANN) called compositional pattern producing networks (CPPNs) with the NeuroEvolution of Augmenting Topologies (NEAT) method for evolving increasingly complex structures. CPPNs in NEAT Drummer input existing human compositions and output an accompanying drum track. The existing musical parts form a scaffold i.e. support structure, for the drum pattern outputs, thereby exploiting the functional relationship of drums to musical parts (e.g. to lead guitar, bru:is, etc.) The results are convincing drum patterns that follow the contours of the original song, validating a new approach to computergenerated music composition.

Notes

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Thesis Completion

2008

Semester

Fall

Advisor

Stanley, Kenneth O.

Degree

Bachelor of Science (B.S.)

College

College of Engineering and Computer Science

Degree Program

Computer Science

Subjects

Dissertations, Academic -- Electrical Engineering and Computer Science;Electrical Engineering and Computer Science -- Dissertations, Academic

Format

Print

Identifier

DP0022251

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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