[P]Building an End-to-End Music Genre Classifier: My first deep dive into Audio Processing and ML.

Building an End-to-End Music Genre Classifier: My first deep dive into Audio Processing and ML.

Hi everyone, ​I’m a 2nd-year Electrical and Electronics Engineering student, and I just finished my first end-to-end project in the intersection of Audio Processing and Machine Learning. ​As someone who is passionate about metal music and embedded systems, I wanted to understand how machines “hear” and categorize different genres. I built a Music Genre Classifier using Python, and it was a great learning experience in what some people call “Vibe Coding”—using LLMs to prototype rapidly while focusing on the underlying engineering logic. ​What I did: ​Data Processing: Used Librosa for feature extraction (MFCCs, Spectrograms, and Mel-scale). ​The Model: Built a classification model (CNN/SVM) to recognize various genres. ​The Workflow: I used AI as a collaborative partner to handle boilerplate code and debugging, which allowed me to focus on the signal processing theory (Fourier Transforms, etc.). ​I’m looking for feedback on: ​Code Architecture: How can I make my Python scripts more modular for future embedded integration? ​Optimization: Are there more efficient ways to handle real-time audio features? ​General Advice: As an EEE student aiming for a master’s in AI/Robotics, what should be my next step to level up this project? ​GitHub Repository: https://github.com/Baturalpbyg/music-genre-classification