AI audio and music generation has evolved from symbolic MIDI synthesis to end-to-end neural models that produce raw audio waveforms with human-like quality and expressiveness. Modern systems leverage transformer architectures, diffusion models, and neural audio codecs to create everything from full songs with vocals to sound effects, voice clones, and instrument separations. Unlike traditional synthesis, these models learn patterns from massive audio datasets, enabling text-to-music generation, style transfer, and real-time manipulation at scales previously impossible. Understanding the distinction between symbolic (MIDI/sheet music) and raw audio generation is fundamental — symbolic models work with discrete note events, while raw audio models handle continuous waveforms at 24kHz+ sample rates, each requiring different architectures and training strategies.