Generative AI for Audio and Images: Models and Applications offers an in-depth exploration of how modern generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion models are used to create, manipulate, and enhance audio, image, and video content.
Generative AI for Audio and Images: Models and Applications
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November 2025
作业
17 项作业
授课语言：英语（English）
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该课程共有4个模块
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.
涵盖的内容
21个视频3篇阅读材料4个作业2个讨论话题
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.
涵盖的内容
31个视频2篇阅读材料4个作业
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.
涵盖的内容
22个视频3篇阅读材料5个作业
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
涵盖的内容
21个视频1篇阅读材料4个作业
从 Algorithms 浏览更多内容
Alberta Machine Intelligence Institute
状态：免费试用
状态：免费试用
状态：免费试用
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