Download or read book Deep Learning Techniques for Music Generation written by Jean-Pierre Briot and published by Springer. This book was released on 2019-11-08 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning and Music Generation written by José M. Iñesta and published by Routledge. This book was released on 2018-10-16 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Hands On Music Generation with Magenta written by Alexandre DuBreuil and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Handbook of Artificial Intelligence for Music written by Eduardo Reck Miranda and published by Springer Nature. This book was released on 2021-07-02 with total page 994 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence in Music Sound Art and Design written by Juan Romero and published by Springer Nature. This book was released on 2020-04-09 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Artificial Intelligence in Music Sound Art and Design written by Juan Romero and published by Springer Nature. This book was released on 2021-04-01 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Algorithmic Composition written by Gerhard Nierhaus and published by Springer Science & Business Media. This book was released on 2009-08-28 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Machine Learning and Music Generation written by José M. Iñesta and published by Routledge. This book was released on 2018-10-16 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Computational Intelligence in Music Sound Art and Design written by Anikó Ekárt and published by Springer. This book was released on 2019-04-10 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Computation in Combinatorial Optimization, EvoMUSART 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover a wide range of topics and application areas, including: visual art and music generation, analysis, and interpretation; sound synthesis; architecture; video; poetry; design; and other creative tasks.
Download or read book Proceedings of International Conference on Computational Intelligence Data Science and Cloud Computing written by Valentina Emilia Balas and published by Springer Nature. This book was released on 2021 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book AI and Machine Learning for Coders written by Laurence Moroney and published by O'Reilly Media. This book was released on 2020-10-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving
Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Download or read book The Algorithmic Composer written by David Cope and published by A-R Editions, Inc.. This book was released on 2000-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accompanying disc contains Melody Predictor (a program), Compose (a program), Fun, Déjà vu (a program), Backtalk, some tutorials, Alice (an interactive program), recorded performances of many of the works presented in the text, and MIDI performances of most of the music in the figures.
Download or read book Sourdough written by Robin Sloan and published by MCD. This book was released on 2017-09-05 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Experiments in Musical Intelligence written by David Cope and published by . This book was released on 1996 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Foundations of Deep Reinforcement Learning written by Laura Graesser and published by Addison-Wesley Professional. This book was released on 2019-11-20 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
