[P]Building an End-to-End Music Genre Classifier: My first deep dive into Audio Processing and ML.
A student's first deep dive shows how AI can rapidly prototype audio ML projects.
Deep Dive
A second-year engineering student built an end-to-end music genre classifier as their first audio processing and machine learning project. Using Python and Librosa for feature extraction (MFCCs, spectrograms), they implemented a CNN/SVM model. The student practiced 'Vibe Coding'—leveraging LLMs to handle boilerplate code and debugging—to focus on core signal processing theory like Fourier Transforms. They are now seeking feedback on code architecture and optimization for real-time embedded systems integration.
Why It Matters
It demonstrates how beginners can use AI tools to accelerate learning and build functional ML projects, lowering the barrier to entry.