Thesis Central

Obscene and indecent content in popular music is becoming more prevalent, the historical unreliability of explicit markers has seemingly worsened with the rise of streaming as the primary method of music consumption, and the tools to remove the explicit content from song lyrics or ’clean’ a song pose a financial burden or require a degree of technical knowledge not common in the general population. This paper compares open source software for Music Source Separation, focusing on the ability to separate vocals from a musical track, and introduces an application exposing the champion through an intuitive graphic user interface. The resulting application allows users to extract vocals from an audio file, selectively remove vocals, and recombine the edited vocals with their source, effectively allowing users to create their own clean versions of their favorite music. The aim is to alleviate the financial burden associated with professional Music Source Separation software as well as increase accessibility of MSS for the layman.

This thesis provides a new resource for squash performance analysis by developing a computer vision system that integrates advanced object detection and tracking techniques. By stringing together YOLOv8 for precise player detection and StrongSORT for multi-object tracking, the system accurately processes game footage to collect player data. A tool developed in this project is a user-assisted manual court mapping interface corrects perspective distortions, providing the resource to generate movement based analytics that reflect on-court dynamics, such as control of the critical ’T’ position. The adaptability of the technologies used create the opportunity for the expansion of this project. Further development of this project offers valuable insight for coaching and performance improvement, and further refinements are expected to enhance the accuracy of detection and tracking even further.

The shift towards personalized content consumption in social media has driven the rise of black-box algorithms which are foundational in delivering tailored experiences. These algorithms do an incredible job at delivering tailored experiences without much effort from the user, however often utilize intrusive methods such as location, watch time, and scrolling behavior. At the same time, alternative social networks have emerged with an added emphasis on decentralization, transparency, and privacy. However, a significant gap remains in this space: the absence of alternative social media applications that provide users with feed curation tools, make these tools accessible to all end-users, and unify fragmented user communities across diverse networks. This paper introduces Pantry, a social media reader application designed to address this gap through the idea of teachable feeds, inspired by existing literature, and powered by an on-device machine learning model. Evaluation through a user study reveals that Pantry succeeds in delivering feed curation tools driven by users and provide several key insights. The results of this paper, from the user study, help inform and advance the future design space.