Hands-On Music Generation with Magenta: Explore the role of deep learning in music generation and assisted music composition
bDesign and use machine learning models for music generation using Magenta and make them interact with existing music creation tools/b h4Key Features/h4 ulliLearn how machine learning, deep learning, and reinforcement learning are used in music generation /li liGenerate new content by manipulating t...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2020
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bDesign and use machine learning models for music generation using Magenta and make them interact with existing music creation tools/b h4Key Features/h4 ulliLearn how machine learning, deep learning, and reinforcement learning are used in music generation /li liGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with it /li liExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth/li/ul h4Book Description/h4 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 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. h4What you will learn/h4 ulliUse RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences /li liUse WaveNet and GAN models to generate instrument notes in the form of raw audio /li liEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences /li liPrepare and create your dataset on specific styles and instruments /li liTrain your network on your personal datasets and fix problems when training networks /li liApply MIDI to synchronize Magenta with existing music production tools like DAWs/li/ul h4Who this book is for/h4 This 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 |
| Beschreibung: | 1 Online-Ressource (360 Seiten) |
| ISBN: | 9781838825768 |
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| 520 | |a bDesign and use machine learning models for music generation using Magenta and make them interact with existing music creation tools/b h4Key Features/h4 ulliLearn how machine learning, deep learning, and reinforcement learning are used in music generation /li liGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with it /li liExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth/li/ul h4Book Description/h4 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 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. | ||
| 520 | |a 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. | ||
| 520 | |a h4What you will learn/h4 ulliUse RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences /li liUse WaveNet and GAN models to generate instrument notes in the form of raw audio /li liEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences /li liPrepare and create your dataset on specific styles and instruments /li liTrain your network on your personal datasets and fix problems when training networks /li liApply MIDI to synchronize Magenta with existing music production tools like DAWs/li/ul h4Who this book is for/h4 This 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 | ||
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| isbn | 9781838825768 |
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| spelling | DuBreuil, Alexandre Verfasser aut Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition DuBreuil, Alexandre 1 Birmingham Packt Publishing Limited 2020 1 Online-Ressource (360 Seiten) txt rdacontent c rdamedia cr rdacarrier bDesign and use machine learning models for music generation using Magenta and make them interact with existing music creation tools/b h4Key Features/h4 ulliLearn how machine learning, deep learning, and reinforcement learning are used in music generation /li liGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with it /li liExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynth/li/ul h4Book Description/h4 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 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. h4What you will learn/h4 ulliUse RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences /li liUse WaveNet and GAN models to generate instrument notes in the form of raw audio /li liEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences /li liPrepare and create your dataset on specific styles and instruments /li liTrain your network on your personal datasets and fix problems when training networks /li liApply MIDI to synchronize Magenta with existing music production tools like DAWs/li/ul h4Who this book is for/h4 This 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 COMPUTERS / Programming Languages / Python COMPUTERS / Neural Networks |
| spellingShingle | DuBreuil, Alexandre Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition COMPUTERS / Programming Languages / Python COMPUTERS / Neural Networks |
| title | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition |
| title_auth | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition |
| title_exact_search | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition |
| title_exact_search_txtP | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition |
| title_full | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition DuBreuil, Alexandre |
| title_fullStr | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition DuBreuil, Alexandre |
| title_full_unstemmed | Hands-On Music Generation with Magenta Explore the role of deep learning in music generation and assisted music composition DuBreuil, Alexandre |
| title_short | Hands-On Music Generation with Magenta |
| title_sort | hands on music generation with magenta explore the role of deep learning in music generation and assisted music composition |
| title_sub | Explore the role of deep learning in music generation and assisted music composition |
| topic | COMPUTERS / Programming Languages / Python COMPUTERS / Neural Networks |
| topic_facet | COMPUTERS / Programming Languages / Python COMPUTERS / Neural Networks |
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