Research Papers

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Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers

May 2021 - arXiv:2105.00173

ABSTRACT: Current computational-emotion research has focused on applying acoustic properties to analyze how emotions are perceived mathematically or used in natural language processing machine learning models. With most recent interest being in analyzing emotions from the spoken voice, little experimentation has been performed to discover how emotions are recognized in the singing voice -- both in noiseless and noisy data (i.e., data that is either inaccurate, difficult to interpret, has corrupted/distorted/nonsense information like actual noise sounds in this case, or has a low ratio of usable/unusable information). Not only does this ignore the challenges of training machine learning models on more subjective data and testing them with much noisier data, but there is also a clear disconnect in progress between advancing the development of convolutional neural networks and the goal of emotionally cognizant artificial intelligence. By training a new model to include this type of information with a rich comprehension of psycho-acoustic properties, not only can models be trained to recognize information within extremely noisy data, but advancement can be made toward more complex biofeedback applications -- including creating a model which could recognize emotions given any human information (language, breath, voice, body, posture) and be used in any performance medium (music, speech, acting) or psychological assistance for patients with disorders such as BPD, alexithymia, autism, among others. This paper seeks to reflect and expand upon the findings of related research and present a stepping-stone toward this end goal.


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Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers

Dec. 2020 - arXiv:2101.00169

ABSTRACT: Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for a generated composition nearly indistinguishable from that of a master composer.


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Optimizing Data Cube Visualization for Web Applications: Performance and User-Friendly Data Aggregation

Dec. 2020 - arXiv:2101.00171

ABSTRACT: Current open source applications which allow for cross-platform data visualization of OLAP cubes feature issues of high overhead and inconsistency due to data oversimplification. To improve upon this issue, there is a need to cut down the number of pipelines that the data must travel between for these aggregation operations and create a single, unified application which performs efficiently without sacrificing data, and allows for ease of usability and extension.


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Visualization Techniques with Data Cubes: Utilizing Concurrency for Complex Data

Oct. 2020 - arXiv:2101.00170

ABSTRACT: With web and mobile platforms becoming more prominent devices utilized in data analysis, there are currently few systems which are not without flaw. In order to increase the performance of these systems and decrease errors of data oversimplification, we seek to understand how other programming languages can be used across these platforms which provide data and type safety, as well as utilizing concurrency to perform complex data manipulation tasks.


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Chunk List

Feb. 2017 - arXiv:2101.00172

ABSTRACT: Chunking data is obviously no new concept; however, I had never found any data structures that used chunking as the basis of their implementation. I figured that by using chunking alongside concurrency, I could create an extremely fast run-time in regards to particular methods as searching and/or sorting. By using chunking and concurrency to my advantage, I came up with the chunk list - a dynamic list-based data structure that would separate large amounts of data into specifically sized chunks, each of which should be able to be searched at the exact same time by searching each chunk on a separate thread. As a result of implementing this concept into its own class, I was able to create something that almost consistently gives around 20x-300x faster results than a regular ArrayList. However, should speed be a particular issue even after implementation, users can modify the size of the chunks and benchmark the speed of using smaller or larger chunks, depending on the amount of data being stored.


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Research Papers

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An Introduction to Film Scoring in Horror Movies

Jan. 2020 - Paperback

DESCRIPTION: The horror movie genre is one of complexity, both in design and story. The style of screenwriting and use of visual and aural nuances has developed into a rich, exciting form of entertainment that plays on the viewer's mind, body, and morals. In terms of visuals, horror movies play on classic psychological fears and utilize them in ways that may be gruesome or traumatizing, yet impossible to look away from. However, one of the biggest tools used to create the sense of fear in the movies is of course the specific use of sound and music to build and develop tension and emphasize horrific scenes. From the usage of drums to imitate a pounding heartbeat to strings playing harsh, discordant and unexpected sounds meant to imitate the screams of frightened animals, and the crashing, staccato chords that strike into instinctive fears, the composer/film scorer of the horror genre plays an extremely important role in delivering the full effect of the film that often goes unnoticed.

ISBN-10: 1794899219

ISBN-13: 978-1794899216


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Undergraduate Musicology Research: Studies in Music History

Nov. 2019 - Paperback

DESCRIPTION: This book contains a collection of three research papers during undergraduate coursework by Daniel Szelogowski. The works recall three lesser-known composers: Francesco Landini, Frederic Chopin, and Karol Szymanowski -- all of which have many sources of misinformation or lack of information overall.

ISBN-10: 1794731350

ISBN-13: 978-1794731356


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