Phase 5: Speech Foundations · ~45 minutes · Python

Audio Fundamentals — Waveforms, Sampling, Fourier Transform

Waveforms are the raw signal. Spectrograms are the representation. Mel features are the ML-friendly form. Every modern ASR and TTS pipeline walks this ladder, and the first rung is understanding sampling and Fourier.

What you will learn

Implement audio fundamentals — waveforms, sampling, fourier transform from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Learn
Languages: Python
Prerequisites: Probability and Distributions (Math for AI & ML), Norms and Distances (Math for AI & ML)
Time: ~45 minutes

The Problem

A microphone produces a pressure-vs-time signal. Your neural net consumes tensors. Between them sits a stack of conventions that, when violated, produce silent bugs: the model trains fine but the WER doubles, or TTS ships a hiss, or a voice cloning system memorizes the microphone instead of the speaker.

Every bug in speech systems traces back to one of three questions:

What sample rate was the data recorded at, and what does the model expect?
Is the signal aliased?
Are you operating on raw samples or on a frequency representation?

Get these right and the rest of this course is tractable. Get them wrong and even Whisper-Large-v4 produces garbage.

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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