Date of Graduation
8-2020
Document Type
Thesis
Degree Name
Bachelor of Science in Industrial Engineering
Degree Level
Undergraduate
Department
Industrial Engineering
Advisor/Mentor
Chimka, Justin
Committee Member/Reader
Rainwater, Chase E.
Abstract
One of the most common tools for evaluating data is regression. This technique, widely used by industrial engineers, explores linear relationships between predictors and the response. Each observation of the response is a fixed linear combination of the predictors with an added error element. The method is built on the assumption that this error is normally distributed across all observations and has a mean of zero. In some cases, it has been found that the inherent variation is not the result of a random variable, but is instead the result of self-symmetric properties of the observations. For data with these characteristics, fractal analysis can be used to explain the variation. There has been evidence from previous work that musical pieces have to some degree a fractal structure, but there remains to be more work done on performing fractal analysis to musical pieces. In this research, a computationally efficient method of performing fractal analysis on time-series data is applied to a musical recording. It is then determined whether this fractal dimension is a suitable measure to distinguish between musical genres.
Keywords
fractal dimension; fractals; music categorization; time-series data; GTZAN; machine learning
Citation
Clark, N. (2020). Analyzing the Fractal Dimension of Various Musical Pieces. Industrial Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/ineguht/74
Included in
Applied Statistics Commons, Categorical Data Analysis Commons, Longitudinal Data Analysis and Time Series Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Other Mathematics Commons
