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- Creators: Barrett, The Honors College
- Member of: Theses and Dissertations
Music streaming services have affected the music industry from both a financial and legal standpoint. Their current business model affects stakeholders such as artists, users, and investors. These services have been scrutinized recently for their imperfect royalty distribution model. Covid-19 has made these discussions even more relevant as touring income has come to a halt for musicians and the live entertainment industry. <br/>Under the current per-stream model, it is becoming exceedingly hard for artists to make a living off of streams. This forces artists to tour heavily as well as cut corners to create what is essentially “disposable art”. Rapidly releasing multiple projects a year has become the norm for many modern artists. This paper will examine the licensing framework, royalty payout issues, and propose a solution.
The esports scene has been constantly evolving ever since its inception in the early 1970s, growing from small arcade based tournaments to the multibillion dollar industry that can be observed today (Bountie Gaming, 2018). In fact, the term esports was not widely used until the early 2000s, decades after the first gaming tournaments had taken place. Decades prior, the earliest large-scale gaming tournament was hosted by Atari in 1980 for the game Space Invaders . While still primitive by today’s standards, games such as Space Invaders inspired fierce competition and effectively laid the foundation for what would grow into the booming industry that we see today (Edwards, 2013).
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
With the recent focus of attention towards remote work and mobile computing, the possibility of taking a powerful workstation wherever needed is enticing. However, even emerging laptops today struggle to compete with desktops in terms of cost, maintenance, and future upgrades. The price point of a powerful laptop is considerably higher compared to an equally powerful desktop computer, and most laptops are manufactured in a way that makes upgrading parts of the machine difficult or impossible, forcing a complete purchase in the event of failure or a component needing an upgrade. In the case where someone already owns a desktop computer and must be mobile, instead of needing to purchase a second device at full price, it may be possible to develop a low-cost computer that has just enough power to connect to the existing desktop and run all processing there, using the mobile device only as a user interface. This thesis will explore the development of a custom PCB that utilizes a Raspberry Pi Computer Module 4, as well as the development of a fork of the Open Source project Moonlight to stream a host machine's screen to a remote client. This implementation will be compared against other existing remote desktop solutions to analyze it's performance and quality.
Anthemy is a web app that I created so that Spotify users could connect with other uses and see their listening statistics. The app has a chat feature that matches concurrent users based on a variety of search criteria, as well as a statistics page that contains a breakdown of a user's top artists, songs, albums, and genres as well as a detailed breakdown of each of their liked playlists.