A Practical Guide to Generative Music AI for Developers April 2025

A Practical Guide to Generative Music AI for Developers April 2025

Lancelot Blanchard


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    • Session 1: An Introduction to AI Music

      • Main Topics: AI Music Case Studies, Course Roadmap
      • An Introduction to AI Music (50')
      • Session 1 PDF handout
      • Open Discussion
    • Session 2: Setting up your environment

      • Main Topics: Environment Setup
      • GitHub Repository
      • Setting up your environment - Cloning the class repository (10')
      • Hands On
      • Setting up your environment - Hands On 1.1: Loading, visualizing, playing audio (10')
      • Setting up your environment - Hands On 1.2: Extracting Audio Features, RMS and ZCR (13')
      • Setting up your environment - Hands On 1.2: Extracting Audio Features, Spectrograms (13')
      • Setting up your environment - Hands On 2: Manipulating MIDI Data (20')
    • Session 3: Core Machine Learning Concepts for Music and Audio

      • Main Topics: Audio vs Symbolic Music, Basics of Generative AI, Data Acquisition and Ethics
      • Statistical Basics of Generative Modeling in Artificial Intelligence (10')
      • Variational Autoencoders (16')
      • Hands On
      • Hands On 1.1: Lakh MIDI Dataset (12')
      • Hands On 1.2: Free Music Archive (6')
      • Hands On 2: Using RAVE
    • Session 4: Real-Life Collaborations between Artists and Engineers with Guest Speaker Jordan Rudess

      • Main Topics: Human-Computer Interaction, Iterative Design, Continuous Deployment
      • Human-Computer Interaction & User-Centered Design
      • Open Discussion with Jordan Rudess
    • Session 5: Representation Learning for Music

      • Deep Dive into MIDI & Spectrograms
      • Comparing Musical Representations & Encodec Deep Dive (14')
      • Understanding RVQ in Encodec (12')
      • Hands On
      • Hands On: Encodec (29')
    • Session 6: Autoregressive Music Generation

      • Main Topics: Autoregressive modeling, the Transformer architecture, HuggingFace Hub
      • The Transformer architecture (15')
      • Understanding Anticipatory Music Transformers (13')
      • Hands On
      • Hands On: Using AMT to generate MIDI data (Part 1) (18')
      • Hands On: Using AMT to generate MIDI data (Part 2) (17')
    • Session 7: Autoregressive Music Generation (Part 2)

      • Main Topics: MusicGen & Audio Generation with Transformers
      • Understanding MusicGen (8')
      • Hands On
      • Hands On: Using MusicGen to generate audio (38')
    • Session 8: Diffusion Models for Music Generation

      • Main Topics: Diffusion Models, Latent Diffusion Models
      • Intro to Diffusion Models Part 1 (11')
      • Intro to Diffusion Models Part 2 (14')
      • Conditioning & Classifier-Free Guidance (10')
      • The UNet Architecture (6')
      • Inference-Time Optimization: DITTO (6')
      • Hands On
      • Hands On: Using Stable Audio Part 1 (15')
      • Hands On: Using Stable Audio Part 2 (18')
    • Session 9: Real-Time Generative AI & Commercial Applications of Generative AI in Music [Guest: Christian Steinmetz]

      • Introduction to TorchScript (18')
      • 8-bit Linear Quantization (9')
      • ONNX and Graph Optimizations (15')
      • Main Topics: Landscape of companies in AI and Music, Available Commercial Products
      • Demo
    • Session 10: Final Project Planning

      • Main Topics: Setting up a project specification, timeline, and scope
      • Training Part 1 (8')
      • Training Part 2 (13')
      • Training Part 3 (16')
      • Peer Review & Feedback
    • Session 11: Final Project Lab

      • Session 11
      • Lab Session: Guided Coding & Troubleshooting
      • Milestone Check-Ins
    • Session 12: Project Showcase & Next Steps