Musical Intelligence
The Project
My project focuses on using AI to help people learn music more effectively. For example, users are able to choose their favorite type of music, whether it is BGM, rap, or pop, and the AI can detect which genre the music fits best using Generative AI and Machine Learning. The system uses neural networks and algorithms trained on music data to analyze patterns such as notes, rhythm, and genre. Examples mentioned in the research include recurrent neural networks (RNNs) and deep learning models that can generate or analyze music based on user input and datasets. One thing that inspired this project was seeing my peers constantly ask the music director to learn songs that matched their own interests rather than only the director’s preferences. This gave me the idea of creating a music learning app centered around the user’s personal taste, making music theory more fun and engaging. The most difficult part of the code was likely the backend and AI analysis pipeline. That section had to connect the server, database, and music-analysis tools together. It was more difficult than the frontend because it needed to correctly process music data, organize API routes, initialize the database, and return accurate results. Even a small error could affect the entire system.
