Wrapping up my Master’s at Full Sail, I finalized SoundSoar, passed my defense, and gathered feedback to push the project further. I refined models, tightened the paper and presentation, and mapped concrete next steps for features, UX, and AI.
Introduction
This month capped months of work as I finalized SoundSoar and prepared for the final presentation. I spent time refining the project, tightening my paper, and polishing my deck. After presenting to the committee and passing, I received helpful feedback that will elevate SoundSoar, especially around feature engineering. The experience was rewarding and motivating, and I’m excited to keep building on their insights. The cover image can serve as a single visual for this post.
Lessons along the way
- Server issues built resilience. Troubleshooting wasn’t glamorous, but it gave me confidence to handle infrastructure problems quickly.
- Scheduling kept the project moving. Breaking work into smaller tasks and automating syncs ensured the data stayed current.
- A solid modeling foundation matters. I’m ready to improve by tailoring features to specific models instead of using one feature set for all.
The role of Full Sail’s curriculum
Every course contributed to SoundSoar and to my growth as an engineer and researcher.
Research Approaches in Computer Science
Helped me structure research, evaluate data sources, and write clearly so my work can be understood and adopted by others.
Advanced Software Engineering
Sharpened time management and delivery processes. I learned to balance deadlines, handle complex projects, and prioritize effectively across coding and deployment.
Data Science
Deepened my understanding of statistics, processing, and machine learning techniques. It connected the dots for building SoundSoar’s trend prediction and sparked more interest in data engineering.
Machine Learning
Gave me algorithms and evaluation methods I could directly apply to train and fine-tune the model.
Human–Computer Interaction (HCI) & HCI App Development
Guided the user-centric interface, ensuring SoundSoar is approachable and practical.
Data Visualization and Extended Reality
Taught me to represent data effectively. We explored matplotlib, seaborn, and bokeh; I ultimately used Plotly for interactivity, but the core visualization principles shaped the UX.
Advanced Artificial Intelligence
Built a foundation in neural networks, deep learning, and reinforcement learning. This is fueling my plan to add a neural network and explore AI features like emotional analysis.
Capstone Courses (Research, Dev I, Dev II, Deployment)
Tied everything together: development, testing, deployment, and iterative feedback from professors and peers.
These courses did more than teach concepts—they built my confidence. With guidance from my advisor, Dr. Andreas Marpaung, and other instructors, I learned to tackle complex problems and trust my ideas.
Moving forward
- Refine the algorithm by tailoring features per model to improve accuracy and balance.
- Integrate a neural network that doesn’t rely on historical popularity data.
- Add AI-powered emotional analysis for songs to deepen insights for creators.
- Improve the front-end with better filters and more intuitive exploration.
Advice for future students
- Expect challenges and use them to learn. Ask questions and seek feedback.
- Connect coursework to something you care about—it keeps you motivated and makes the work real.
- Build community with professors and peers; support accelerates growth.
For me, that passion includes animal welfare and entrepreneurship. I co-founded TheDoodPack with two close friends—we rescue and rehome pets, and version 4 of our website is now live. The idea for SoundSoar grew from our content needs: we asked, “Which song should we use?” and I wanted a tool that helps creators save time and increase engagement.
Reflection video
I recorded a short reflection on my journey through the program and how it shaped SoundSoar and what’s next. See the video attached in this post for more.
