This module introduces the foundations and core concepts of AI-generated audio. Learners explore why audio generation is uniquely challenging, such representation and evaluation challenges. They learn how audio is represented and processed, compare waveform and symbolic formats, and common audio data formats and Python libraries for working with audio. The module also examines methods for evaluating generated audio and provides a framework for categorizing audio generation approaches by their functionality and human–AI collaboration level. It concludes with a historical overview of AI-generated audio, tracing its evolution from early rule-based methods to modern deep generative models.

Building on the fundamentals, this module dives into advanced models for audio generation. Learners study Variational Autoencoders (VAEs) and their variants, and how they apply to melody generation and speech synthesis. The module also explores transformer-based models, such as Music Transformer, AudioLM, and FastSpeech, as well as diffusion-based models like DiffWave and Stable Audio. Through these lessons, learners gain a comprehensive understanding of how modern generative architectures produce realistic, high-quality audio and music.

This module transitions from audio to image generation, introducing the principles and evolution of image and video synthesis. Learners examine key architectures like GANs and VAEs, explore how adversarial training works, and study variations such as Conditional and Progressive GANs, Pix2Pix, and CycleGAN. The module also connects theory to practice by showcasing creative and commercial applications—from art and design to data augmentation—demonstrating how generative models enhance realism and variety in visual outputs.

In this module,we explore the final stages of what large language models (LLMs) can offer. You’ll learn how and when to use fine-tuning, along with the pros and cons of different approaches. Throughout the course, you will receive relevant assignments that prepare you for the capstone project: building a fully functional chatbot