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Creative Coding
A Practical Guide to Generative Music AI for Developers October 2025
Level
Beginner
Duration
25h 53m of video content
Format
Self-paced video
Course overview
Course content
Session 1: An Introduction to AI Music
2 videos, 1 resource, 2 lessons
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Session 1 Recording
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Main Topics: AI Music Case Studies, Course Roadmap
An Introduction to AI Music (50')
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Session 1 PDF handout
Open Discussion
Session 2: Setting up your environment
6 videos, 3 lessons
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Session 2 Recording
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Main Topics: Environment Setup
Setting up your environment - Cloning the class repository (10')
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GitHub Repository
Hands On
Setting up your environment - Hands On 1.1: Loading, visualizing, playing audio (10')
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Setting up your environment - Hands On 1.2: Extracting Audio Features, RMS and ZCR (13')
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Setting up your environment - Hands On 1.2: Extracting Audio Features, Spectrograms (13')
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Setting up your environment - Hands On 2: Manipulating MIDI Data (20')
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Session 3: Core Machine Learning Concepts for Music and Audio
6 videos, 2 lessons
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Session 3 Recording
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Main Topics: Audio vs Symbolic Music, Basics of Generative AI, Data Acquisition and Ethics
Statistical Basics of Generative Modeling in Artificial Intelligence (10')
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Variational Autoencoders (16')
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Hands On
Hands On 1.1: Lakh MIDI Dataset (12')
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Hands On 1.2: Free Music Archive (6')
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Hands On 2: Using RAVE
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Session 4: Real-Life Collaborations between Artists and Engineers with Guest Speaker Jordan Rudess
2 videos, 2 lessons
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Session 4 Recording
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Main Topics: Human-Computer Interaction, Iterative Design, Continuous Deployment
Human-Computer Interaction & User-Centered Design
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Open Discussion with Jordan Rudess
Session 5: Representation Learning for Music
4 videos, 2 lessons
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Session 5 Recording
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Deep Dive into MIDI & Spectrograms
Comparing Musical Representations & Encodec Deep Dive (14')
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Understanding RVQ in Encodec (12')
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Hands On
Hands On: Encodec (29')
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Session 6: Autoregressive Music Generation
5 videos, 2 lessons
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Session 6 Recording
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Main Topics: Autoregressive modeling, the Transformer architecture, HuggingFace Hub
The Transformer architecture (15')
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Understanding Anticipatory Music Transformers (13')
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Hands On
Hands On: Using AMT to generate MIDI data (Part 1) (18')
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Hands On: Using AMT to generate MIDI data (Part 2) (17')
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Session 7: Autoregressive Music Generation (Part 2)
3 videos, 2 lessons
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Session 7 Recording
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Main Topics: MusicGen & Audio Generation with Transformers
Understanding MusicGen (8')
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Hands On
Hands On: Using MusicGen to generate audio (38')
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Session 8: Diffusion Models for Music Generation
8 videos, 2 lessons
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Session 8 Recording
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Main Topics: Diffusion Models, Latent Diffusion Models
Intro to Diffusion Models Part 1 (11')
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Intro to Diffusion Models Part 2 (14')
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Conditioning & Classifier-Free Guidance (10')
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The UNet Architecture (6')
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Inference-Time Optimization: DITTO (6')
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Hands On
Hands On: Using Stable Audio Part 1 (15')
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Hands On: Using Stable Audio Part 2 (18')
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Session 9: Commercial Applications of Generative AI in Music with Guest Speaker
3 lessons
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Zoom link - Tuesday 27th May 4pm UK
Main Topics: Landscape of companies in AI and Music, Available Commercial Products
Demo
Session 10: Final Project Planning
3 lessons
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Zoom link - Thursday 29th May 4pm UK
Main Topics: Setting up a project specification, timeline, and scope
Peer Review & Feedback
Session 11: Final Project Lab
3 lessons
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Zoom link - Tuesday 3rd June 4pm UK
Lab Session: Guided Coding & Troubleshooting
Milestone Check-Ins
Session 12: Project Showcase & Next Steps
3 lessons
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Zoom link - Thursday 5th June 4pm UK
Final Presentations
Next Steps
Instructors
Lancelot Blanchard
Instructor
Lancelot Blanchard is a musician, engineer, and AI researcher at the MIT Media Lab's Responsive Environments group. His research focuses on generative AI systems for human-AI co-created live musical performances, pioneering the concept of Symbiotic Virtuosity in AI-human music collaboration. He has collaborated with Grammy-winning keyboardist Jordan Rudess and brings eight years of software engineering experience to his teaching.
