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Portfolio - Experiments with Traditional Structures

Description
The fundamental concept that I have developed and applied throughout my college career is to try to discover innovative ways to combine the experimental production techniques that I learned in my classes with more traditional songwriting structures. In doing so, I explore the line that distinguishes the two from each…

The fundamental concept that I have developed and applied throughout my college career is to try to discover innovative ways to combine the experimental production techniques that I learned in my classes with more traditional songwriting structures. In doing so, I explore the line that distinguishes the two from each other and instill a foreign, yet familiar feeling within the listener. With this approach in mind, I created audio for a variety of media and attempted to push myself in terms of genre and production, ultimately allowing myself to survey a multitude of instruments and audio effects outside of what I learned in my classes. In my portfolio, I have an organized layout of my audio work within the categories of film soundtracks, game audio, and original music, along with how to contact me and information about the licensing of my music. In learning how to create a professional online portfolio, I learned more about the business side of music and where I stand regarding how people listen to my music or use it within their own projects. The process of creating my portfolio taught me a lot about the relationships that I want to pursue with artists that I work with in the future. My portfolio can be found at: markusrennemann.weebly.com
ContributorsRennemann, Markus Horst Florian (Author) / Ingalls, Todd (Thesis director) / Paine, Garth (Committee member) / Barrett, The Honors College (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
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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