Magenta and Music Generation

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

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Hours :

Hands on Labs

Requirements

  • 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
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    Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
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Description

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

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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.