Machine Learning and Music Generation

Machine Learning and Music Generation PDF Author: José M. Iñesta
Publisher: Routledge
ISBN: 1351234528
Category : Mathematics
Languages : en
Pages : 144

Get Book Here

Book Description
Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

Deep Learning Techniques for Music Generation

Deep Learning Techniques for Music Generation PDF Author: Jean-Pierre Briot
Publisher: Springer
ISBN: 3319701630
Category : Computers
Languages : en
Pages : 303

Get Book Here

Book Description
This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Hands-On Music Generation with Magenta

Hands-On Music Generation with Magenta PDF Author: Alexandre DuBreuil
Publisher: Packt Publishing Ltd
ISBN: 1838825762
Category : Mathematics
Languages : en
Pages : 348

Get Book Here

Book Description
Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools Key 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 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 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 for 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.

Generative Deep Learning

Generative Deep Learning PDF Author: David Foster
Publisher: "O'Reilly Media, Inc."
ISBN: 1492041890
Category : Computers
Languages : en
Pages : 301

Get Book Here

Book Description
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Algorithmic Composition

Algorithmic Composition PDF Author: Gerhard Nierhaus
Publisher: Springer Science & Business Media
ISBN: 3211755403
Category : Mathematics
Languages : en
Pages : 294

Get Book Here

Book Description
Algorithmic composition – composing by means of formalizable methods – has a century old tradition not only in occidental music history. This is the first book to provide a detailed overview of prominent procedures of algorithmic composition in a pragmatic way rather than by treating formalizable aspects in single works. In addition to an historic overview, each chapter presents a specific class of algorithm in a compositional context by providing a general introduction to its development and theoretical basis and describes different musical applications. Each chapter outlines the strengths, weaknesses and possible aesthetical implications resulting from the application of the treated approaches. Topics covered are: markov models, generative grammars, transition networks, chaos and self-similarity, genetic algorithms, cellular automata, neural networks and artificial intelligence are covered. The comprehensive bibliography makes this work ideal for the musician and the researcher alike.

Artificial Intelligence in Music, Sound, Art and Design

Artificial Intelligence in Music, Sound, Art and Design PDF Author: Juan Romero
Publisher: Springer Nature
ISBN: 3030438597
Category : Computers
Languages : en
Pages : 238

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoCOP and EvoApplications. The 15 revised full papers presented were carefully reviewed and selected from 31 submissions. The papers cover a wide spectrum of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.

Artificial Intelligence in Music, Sound, Art and Design

Artificial Intelligence in Music, Sound, Art and Design PDF Author: Juan Romero
Publisher: Springer Nature
ISBN: 3030729141
Category : Computers
Languages : en
Pages : 501

Get Book Here

Book Description
This book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021, held as part of Evo* 2021, as Virtual Event, in April 2021, co-located with the Evo* 2021 events, EvoCOP, EvoApplications, and EuroGP. The 24 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture.

Handbook of Artificial Intelligence for Music

Handbook of Artificial Intelligence for Music PDF Author: Eduardo Reck Miranda
Publisher: Springer Nature
ISBN: 3030721167
Category : Computers
Languages : en
Pages : 1007

Get Book Here

Book Description
This book presents comprehensive coverage of the latest advances in research into enabling machines to listen to and compose new music. It includes chapters introducing what we know about human musical intelligence and on how this knowledge can be simulated with AI. The development of interactive musical robots and emerging new approaches to AI-based musical creativity are also introduced, including brain–computer music interfaces, bio-processors and quantum computing. Artificial Intelligence (AI) technology permeates the music industry, from management systems for recording studios to recommendation systems for online commercialization of music through the Internet. Yet whereas AI for online music distribution is well advanced, this book focuses on a largely unexplored application: AI for creating the actual musical content.

Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing

Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing PDF Author: Valentina E. Balas
Publisher: Springer Nature
ISBN: 9813349689
Category : Technology & Engineering
Languages : en
Pages : 781

Get Book Here

Book Description
This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25–27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.

Sourdough

Sourdough PDF Author: Robin Sloan
Publisher: MCD
ISBN: 0374716439
Category : Fiction
Languages : en
Pages : 273

Get Book Here

Book Description
From Robin Sloan, the New York Times bestselling author of Mr. Penumbra’s 24-Hour Bookstore, comes Sourdough, "a perfect parable for our times" (San Francisco Magazine): a delicious and funny novel about an overworked and under-socialized software engineer discovering a calling and a community as a baker. Named One of the Best Books of the Year by NPR, the San Francisco Chronicle, and Southern Living Lois Clary is a software engineer at General Dexterity, a San Francisco robotics company with world-changing ambitions. She codes all day and collapses at night, her human contact limited to the two brothers who run the neighborhood hole-in-the-wall from which she orders dinner every evening. Then, disaster! Visa issues. The brothers quickly close up shop. But they have one last delivery for Lois: their culture, the sourdough starter used to bake their bread. She must keep it alive, they tell her—feed it daily, play it music, and learn to bake with it. Lois is no baker, but she could use a roommate, even if it is a needy colony of microorganisms. Soon, not only is she eating her own homemade bread, she’s providing loaves to the General Dexterity cafeteria every day. Then the company chef urges her to take her product to the farmer’s market—and a whole new world opens up.