Hands-On Music Generation with Magenta

Dubreuil, Alexandre

  • 出版商: Packt Publishing
  • 出版日期: 2020-01-30
  • 售價: $1,420
  • 貴賓價: 9.5$1,349
  • 語言: 英文
  • 頁數: 360
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838824413
  • ISBN-13: 9781838824419
  • 相關分類: DeepLearningMachine Learning
  • 立即出貨 (庫存=1)

買這商品的人也買了...

商品描述

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.

  • 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
  • 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

商品描述(中文翻譯)

機器學習(ML)在藝術中的重要性因該領域的最新進展而快速增長,而Magenta正處於這一創新的前沿。通過本書,您將以實踐的方式使用ML模型進行音樂生成,學習如何將其整合到現有的音樂製作工作流程中。本書包含實際示例和解釋理解底層技術所需的理論背景,是開始探索音樂生成的完美起點。

本書將幫助您學習如何使用Magenta中的模型生成MIDI打擊、單音和多音旋律,以及原始音頻中的樂器聲音。通過實際示例和深入解釋,您將了解RNN、VAE和GAN等ML模型。利用這些知識,您將創建並訓練自己的模型,用於高級音樂生成用例,並準備新的數據集。最後,您將掌握將Magenta與其他技術(如數字音頻工作站(DAW))集成以及使用Magenta.js在瀏覽器中分發音樂生成應用程序的技巧。

通過閱讀本書,您將熟悉Magenta並掌握使用ML模型以自己的風格進行音樂生成所需的技能。

本書的內容包括:
- 使用Magenta中的RNN模型生成MIDI打擊、單音和多音序列
- 使用WaveNet和GAN模型生成原始音頻形式的樂器音符
- 使用MusicVAE和GrooVAE等變分自編碼器模型對現有序列進行取樣、插值和人性化處理
- 準備並創建特定風格和樂器的數據集
- 在個人數據集上訓練您的網絡並解決訓練網絡時的問題
- 使用MIDI將Magenta與現有音樂製作工具(如DAW)同步
- 了解機器學習、深度學習和強化學習在音樂生成中的應用
- 通過使用Magenta工具操縱源數據生成新內容,並用其訓練機器學習模型
- 探索各種Magenta項目,如Magenta Studio、MusicVAE和NSynth