Phase 5: Speech Foundations · ~75 minutes · Python

Audio Classification — From k-NN on MFCCs to AST and BEATs

Everything from "dog barking vs siren" to "which language is this" is audio classification. The features are mels. The architecture moves each decade. The evaluation stays AUC, F1, and per-class recall.

What you will learn

Implement audio classification — from k-nn on mfccs to ast and beats from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Spectrograms, Mel Scale & Audio Features, Optimizers (Deep Learning from Scratch), CNNs and RNNs for Text
Time: ~75 minutes

The Problem

You get a 10-second clip. You want to know: "what is it?" Urban sound (siren, drill, dog), speech command (yes/no/stop), language ID (en/es/ar), speaker emotion (angry/neutral), or environmental sound (indoor/outdoor, babble). All of these are audio classification, and in 2026 the baseline architecture is mature: log-mel → CNN or Transformer → softmax.

The core difficulty is not the network. It is data. Audio datasets have brutal class imbalance, strong domain shift (clean vs noisy), and label noise (who decided "urban babble" vs "restaurant noise"?). The 80% of the problem is curation, augmentation, and evaluation, not swapping CNN for Transformer.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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