A comprehensive, self-contained guide to how machines learn to compose and synthesize music — from symbolic note-by-note models to the neural-audio…

Welcome to Music Generation: From Notes to Neural Audio.

A comprehensive, self-contained guide to how machines learn to compose and synthesize music — from symbolic note-by-note models to the neural-audio-codec language models and latent audio diffusion systems behind today's text-to-music tools. This is the fourth volume in a series; it blends intuition, mathematics, and runnable code, and builds on its companions on machine learning, image generation, and video generation.

This title is part of the ShriIra library and is free to read in full, right here — our small contribution to making world-class knowledge easy to reach.

A note on reading it: open the Contents menu at the top of the reader to jump between chapters, use the Aa menu to set a comfortable text size, theme (light, sepia, or night), and single- or two-page layout. Your place is saved automatically, so you can always pick up where you left off.

We hope it serves you well.

— Shriira Press

Contents

1. Chapter 1 — What Is Music Generation?
2. Chapter 2 — Sound, Music, and the Ear
3. Chapter 3 — Representing Music for Machines
4. Chapter 4 — Sequence Models for Symbolic Music
5. Chapter 5 — The Music Transformer and Long-Range Structure
6. Chapter 6 — Generating Raw Audio and Neural Audio Codecs
7. Chapter 7 — Autoregressive Audio Language Models
8. Chapter 8 — Diffusion Models for Audio
9. Chapter 9 — Conditioning and Control
10. Chapter 10 — Musical Structure and Long-Form Coherence
11. Chapter 11 — Evaluating Generated Music
12. Chapter 12 — Systems, Real-Time, and Deployment
13. Chapter 13 — Ethics, Copyright, and the Music Industry
14. Appendix A — Notation and Symbols
15. Appendix B — Further Reading