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Music Generation with Magenta

  • Course Code: Data Science - Music Generation with Magenta
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Magenta skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this course, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this course is the perfect starting point to begin exploring music generation. The course will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser.. 

Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore: 

  • Learn how machine learning, deep learning, and reinforcement learning are used in music generation 
  • Generate new content by manipulating the source data using Magenta utilities, and train machine learning models with it 
  • Explore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences 
  • Use WaveNet and GAN models to generate instrument notes in the form of raw audio 
  • Employ Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences 
  • Prepare and create your dataset on specific styles and instruments 
  • Train your network on your personal datasets and fix problems when training networks 
  • Apply MIDI to synchronize Magenta with existing music production tools like DAWs 

Audience & Pre-Requisites 

This course is geared for attendees with Machine Learning on Google Cloud Platform skills who wish to l be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills, and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Section 1: Introduction to Artwork Generation 
  1. Introduction to Magenta and Generative Art 
  • Technical requirements 
  • Overview of generative art 
  • New techniques with machine learning 
  • Google’s Magenta and TensorFlow in music generation 
  • Installing Magenta and Magenta for GPU 
  • Installing the music software and synthesizers 
  • Installing the code editing software 
  • Generating a basic MIDI file 
  1. Section 2: Music Generation with Machine Learning 
  1. Generating Drum Sequences with the Drums RNN 
  • Technical requirements 
  • The significance of RNNs in music generation 
  • Using the Drums RNN on the command line 
  • Using the Drums RNN in Python 
  1. Generating Polyphonic Melodies 
  • Technical requirements 
  • LSTM for long-term dependencies 
  • Generating melodies with the Melody RNN 
  • Generating polyphony with the Polyphony RNN and Performance RNN 
  1. Latent Space Interpolation with MusicVAE 
  • Technical requirements 
  • Continuous latent space in VAEs 
  • Score transformation with MusicVAE and GrooVAE 
  • Understanding TensorFlow code 
  1. Audio Generation with NSynth and GANSynth 
  • Technical requirements 
  • Learning about WaveNet and temporal structures for music 
  • Neural audio synthesis with NSynth 
  • Using GANSynth as a generative instrument 
  1. Section 3: Training, Learning, and Generating a Specific Style 
  1. Data Preparation for Training 
  • Technical requirements 
  • Looking at existing datasets 
  • Building a dance music dataset 
  • Building a jazz dataset 
  • Preparing the data using pipelines 
  1. Training Magenta Models 
  • Technical requirements 
  • Choosing the model and configuration 
  • Training and tuning a model 
  • Using Google Cloud Platform 
  1. Section 4: Making Your Models Interact with Other Applications 
  1. Magenta in the Browser with Magenta.js 
  • Technical requirements 
  • Introducing Magenta.js and TensorFlow.js 
  • Creating a Magenta.js web application 
  • Making Magenta.js interact with other apps 
  1. Making Magenta Interact with Music Applications 
  • Technical requirements 
  • Sending MIDI to a DAW or synthesizer 
  • Looping the generated MIDI 
  • Using Magenta as a standalone application with Magenta Studio 
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