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.

Hiring signal: Understanding of audio classification — from k-nn on mfccs to ast and beats internals

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: Phase 6 · 02 (Spectrograms & Mel), Phase 3 · 06 (CNNs), Phase 5 · 08 (CNNs & 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.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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