Audio Classification EDA

An end-to-end audio classification pipeline — from raw sound to a trained, evaluated model — that reached 88% accuracy on held-out test data.

The pipeline

Every stage, built out properly rather than hand-waved:

• Data collection and organization of the audio corpus
• Preprocessing to clean and normalize the signals
• Feature extraction using MFCC (Mel-Frequency Cepstral Coefficients) — the standard way to turn a waveform into features a model can actually learn from
• Model development and evaluation against a test set

Why MFCC

Raw audio is a huge, noisy time series. MFCCs compress each clip into a compact set of coefficients that capture the parts of the sound humans actually perceive as timbre — which made the downstream model both faster to train and more accurate than feeding it raw or naïvely-windowed audio.

Stack: Librosa · PyDub · Pandas · Matplotlib.