Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Electronic Music Composition and Production

Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial…

This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Investigation and Analysis of Music Genre Identification via Machine Learning

Description
Modern audio datasets and machine learning software tools have given researchers a deep understanding into Music Information Retrieval (MIR) applications. In this paper, we investigate the accuracy and viability of using a machine learning based approach to perform music genre recognition using the Free Music Archive (FMA) dataset. We…

Modern audio datasets and machine learning software tools have given researchers a deep understanding into Music Information Retrieval (MIR) applications. In this paper, we investigate the accuracy and viability of using a machine learning based approach to perform music genre recognition using the Free Music Archive (FMA) dataset. We compare the classification accuracy of popular machine learning models, implement various tuning techniques including principal components analysis (PCA), as well as provide an analysis of the effect of feature space noise on classification accuracy.
ContributorsKhondoker, Farib (Co-author) / Wildenstein, Diego (Co-author) / Spanias, Andreas (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05