Use of Linear Discriminant Analysis in Song Classification: Modeling Based on Wilco Albums
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University of Mississippi eGrove Honors College (Sally McDonnell Barksdale Honors Theses Honors College) Spring 5-1-2021 Use of Linear Discriminant Analysis in Song Classification: Modeling Based on Wilco Albums Caroline Pollard Follow this and additional works at: https://egrove.olemiss.edu/hon_thesis Part of the Applied Statistics Commons, Categorical Data Analysis Commons, and the Statistical Models Commons Recommended Citation Pollard, Caroline, "Use of Linear Discriminant Analysis in Song Classification: Modeling Based on Wilco Albums" (2021). Honors Theses. 1909. https://egrove.olemiss.edu/hon_thesis/1909 This Undergraduate Thesis is brought to you for free and open access by the Honors College (Sally McDonnell Barksdale Honors College) at eGrove. It has been accepted for inclusion in Honors Theses by an authorized administrator of eGrove. For more information, please contact [email protected]. USE OF LINEAR DISCRIMINANT ANALYSIS IN SONG CLASSIFICATION: MODELING BASED ON WILCO ALBUMS By Caroline Pollard A thesis submitted to the faculty of The University of Mississippi in partial fulfillment of the requirements of the Sally McDonnell Barksdale Honors College. Oxford, MS May 2021 Approved By ______________________________ Advisor: Professor John Latartara ______________________________ Reader: Professor Gerard Buskes ______________________________ Reader: Professor Michael Worthy © 2021 Caroline Pollard ALL RIGHTS RESERVED ACKNOWLEDGEMENTS I would like to extend a big thank-you to my readers, Dr. Buskes and Dr. Worthy, and especially my advisor, Dr. Latartara. It has such a pleasure working under and with these knowledgeable professors. They have kindly offered guidance and critiques through this process, and my argument and writing would not be as strong without their work on this as well. It is truly valuable to learn from those much wiser. ABSTRACT CAROLINE POLLARD: Use of Linear Discriminant Analysis in Song Classification (Under the direction of John Latartara) The study of music recommender algorithms is a relatively new area of study. Although these algorithms serve a variety of functions, they primarily help advertise and suggest music to users on music streaming services. This thesis explores the use of linear discriminant analysis in music categorization for the purpose of serving as a cheaper and simpler content-based recommender algorithm. The use of linear discriminant analysis was tested by creating linear discriminant functions that classify Wilco’s songs into their respective albums, specifically A.M., Yankee Hotel Foxtrot, and Sky Blue Sky. 4 sample songs were chosen from each album, and song data was collected from these samples to create the model. These models were tested for accuracy by testing the other, non-sample, songs from the albums. After testing these models, all proved to have an accuracy rate of over 80%. Not being able to write computer code for this algorithm was a limiting factor for testing applicability on a larger-scale, but the small-scale model proves to classify accurately. I predict this accuracy to hold on a larger-scale because it was tested on very similar music when in reality, these models work to classify a diverse range of music. TABLE OF CONTENTS List of Figures……………………………………………………………………………………..2 Introduction………………………………………………………………………………………..4 Chapter 1: Literature Review……………………………………………………………………...7 Chapter 2: Methodology…………………………………………………………………………19 Chapter 3: Analysis……………………………………………………………………………....23 Chapter 4: Discussion and Conclusion…………………………………………………………..66 Bibliography……………………………………………………………………………………..70 1 LIST OF FIGURES Figure 1 ....................................................................................................................................................... 12 Figure 2 ....................................................................................................................................................... 12 Figure 3 ....................................................................................................................................................... 14 Figure 4 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