Hands-On Music Generation with Magenta

Design and use machine learning models for music generation using Magenta and make them interact with existing music creation toolsKey FeaturesLearn how machine learning deep learning and reinforcement learning are used in music generationGenerate new content by manipulating the source data using Magenta utilities and train machine learning models with itExplore various Magenta projects such as Magenta Studio MusicVAE and NSynthBook DescriptionThe 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 book 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 book is the perfect starting point to begin exploring music generation.The book 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.By the end of this book you'll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style.What you will learnUse RNN models in Magenta to generate MIDI percussion and monophonic and polyphonic sequencesUse WaveNet and GAN models to generate instrument notes in the form of raw audioEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample interpolate and humanize existing sequencesPrepare and create your dataset on specific styles and instrumentsTrain your network on your personal datasets and fix problems when training networksApply MIDI to synchronize Magenta with existing music production tools like DAWsWho this book is forThis book is for technically inclined artists and musically inclined computer scientists. Readers who want to get hands-on with building generative music applications that use deep learning will also find this book useful. Although prior musical or technical competence is not required basic knowledge of the Python programming language is assumed. About the Author Alexandre DuBreuil is a software engineer and generative music artist. Through collaborations with bands and artists he has worked on many generative art projects such as generative video systems for music bands in concerts that create visuals based on the underlying musical structure a generative drawing software that creates new content based on a previous artist's work and generative music exhibits in which the generation is based on real-time events and data. Machine learning has a central role in his music generation projects and Alexandre has been using Magenta since its release for inspiration music production and as the cornerstone for making autonomous music generation systems that create endless soundscapes.