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Author: Alexandre DuBreuil
Publisher: Packt Publishing Ltd
ISBN: 1838825762
Category : Mathematics
Languages : en
Pages : 348
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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.
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.
Author: Mitsuko Aramaki
Publisher: Springer Nature
ISBN: 303135382X
Category : Computers
Languages : en
Pages : 338
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Book Description
This book constitutes the refereed proceedings and revised selected papers from the 15th International Symposium on Music in the AI Era, CMMR 2021, which took place during November 15–19, 2021 as a virtual event. The 24 full papers included in this book were carefully reviewed and selected from 48 submissions. The papers are grouped in thematical sessions on Music technology in the IA era; Interactive systems for music; Music Information Retrieval and Modeling; and Music and Performance Analysis.
Author: Jay Asher
Publisher: Packt Publishing Ltd
ISBN: 1800561458
Category : Computers
Languages : en
Pages : 304
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Book Description
A practical guide that takes you from understanding the fundamentals of Logic Pro to discovering professional music creation techniques with an easy-to-follow approach Key FeaturesExplore the world of music production by getting up to speed with Logic ProUnderstand the fundamentals of music production such as recording, editing, and adding effects to musicLearn to produce virtual sounds and music effects to enhance your music and create a final master from a raw music fileBook Description Logic Pro is Apple's flagship application for music creation, found in many professional music studios across the globe. It is a powerful digital audio workstation that comes with all the software tools that you need to create music that sounds great. In the previous version, Logic Pro 10.5, Apple had added impressive features to what was already a full package of tools, loops, FX plug-ins, and software instruments. Providing a comprehensive introduction if you're new to Mac computer music creation, this practical guide will show you how to use Logic Pro and have you up to speed in no time. You'll not only understand what Apple's Logic Pro software can do but also get hands-on with using it to accomplish various musical tasks. The book starts by getting you up and running with the basic terminologies. As you progress, you'll explore how to create audio and MIDI musical parts. To build on your knowledge further, the book will guide you through developing an automated mix. In addition to this, you'll learn how to bounce mixes and audio files for distribution. By the end of this book, you'll be well-versed with Logic Pro and have the skills you need to create professional-quality music. What you will learnGet to grips with Audio and MIDI and how they are different, along with covering Apple LoopsRecord and edit audio, such as your voice or guitarCreate and edit MIDI parts, using Logic Pro's software instrumentsDevelop realistic drums and electronic drums with Logic Pro 10.5's amazing DrummerExplore the new Step Sequencer, Live Loops, and Quick Sampler that were included with version 10.5Edit your arrangement and prepare the parts for mixingDiscover the principles of good mixing, including automation, pre-mastering, and final bouncingWho this book is for This book is for musicians, songwriters, and music producers who want to learn Logic Pro from scratch with the help of expert guidance. A basic understanding of music theories such as chords and notes is highly recommended before you get started. This Logic Pro book also assumes that you'll be working on a Mac.
Author: Jay Asher
Publisher: Packt Publishing Ltd
ISBN: 1800562772
Category : Computers
Languages : en
Pages : 304
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Book Description
A practical guide that takes you from understanding the fundamentals of Logic Pro X to discovering professional music creation techniques with an easy-to-follow approach Key FeaturesExplore the world of music production by getting up to speed with Logic Pro XUnderstand the fundamentals of music production such as recording, editing, and adding effects to musicLearn to produce virtual sounds and music effects to enhance your music and create a final master from a raw music fileBook Description Logic Pro X is Apple's flagship application for music creation, found in many professional music studios across the globe. It is a powerful digital audio workstation that comes with all the software tools that you need to create music that sounds great. In the latest version, Logic Pro X 10.5, Apple has added impressive features to what was already a full package of tools, loops, FX plug-ins, and software instruments. Providing a comprehensive introduction if you're new to Mac computer music creation, this practical guide will show you how to use Logic Pro X and have you up to speed in no time. You'll not only understand what Apple's Logic Pro X software can do but also get hands-on with using it to accomplish various musical tasks. The book starts by getting you up and running with the basic terminologies. As you progress, you'll explore how to create audio and MIDI musical parts. To build on your knowledge further, the book will guide you through developing an automated mix. In addition to this, you'll learn how to bounce mixes and audio files for distribution. By the end of this book, you'll be well-versed with Logic Pro X and have the skills you need to create professional-quality music. What you will learnGet to grips with Audio and MIDI and how they are different, along with covering Apple LoopsRecord and edit audio, such as your voice or guitarCreate and edit MIDI parts, using Logic Pro X's software instrumentsDevelop realistic drums and electronic drums with Logic Pro X 10.5's amazing DrummerExplore the new Step Sequencer, Live Loops, and Quick Sampler that are now included with version 10.5Edit your arrangement and prepare the parts for mixingDiscover the principles of good mixing, including automation, pre-mastering, and final bouncingWho this book is for This book is for musicians, songwriters, and music producers who want to learn Logic Pro X from scratch with the help of expert guidance. A basic understanding of music theories such as chords and notes is highly recommended before you get started. This Logic Pro X book also assumes that you'll be working on a Mac.
Author: Adam Safron
Publisher: Frontiers Media SA
ISBN: 2832536166
Category : Science
Languages : en
Pages : 392
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Book Description
Even before the deep learning revolution, the landscape of artificial intelligence (AI) was already changing drastically in the 90s. Embodied intelligence, it was proposed, must play a crucial role in the design of intelligent machines. This new wave was inspired by what is today known as Embodied and Enactive Cognitive Science or E-Cognition, which considers that cognitive activity does not reduce to the intellectual capacities of agents being able to represent their environments. E-cognition set AI and robotics in a new direction, in which intelligent machines are required to interact with the environment, and where this interaction does not reduce to explicit representations or prespecified algorithms. These ideas revolutionized the way we think about intelligent machines and cognition, but these theoretical advances are only partially reflected in modern approaches to AI and machine learning (ML). Despite deeply impressive achievements, AI/ML still struggles to recapitulate the kinds of intelligence we find in natural systems, whether we are considering individual insects (e.g. simultaneous localization and mapping), or swarm behaviour (e.g. forum sensing and ensemble inferences), and especially the kinds of flexibility and high-level reasoning characteristic of human cognition.
Author: Peter Brinkmann
Publisher: "O'Reilly Media, Inc."
ISBN: 1449314902
Category : Computers
Languages : en
Pages : 123
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Book Description
Pure Data (Pd) is a graphical programming environment for audio and more; libpd is a wrapper that turns Pd into a portable, embeddable audio library. The purpose of this book is to present tools and techniques for using Pure Data and libpd as an audio engine in mobile apps (for Android and iOS).
Author: Zbigniew W. Ras
Publisher: Springer Nature
ISBN: 3030664503
Category : Technology & Engineering
Languages : en
Pages : 236
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Book Description
Music recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media contributes to more accurate inference of the user’s musical preferences and the same to quality of personalized systems. Health recommendation systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. Medical doctors deal with various kinds of diseases in which the music therapy helps to improve symptoms. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The book presents a variety of approaches useful to create recommendation systems in healthcare, music, and in music therapy.
Author: Jean-Pierre Briot
Publisher: Springer
ISBN: 3319701630
Category : Computers
Languages : en
Pages : 303
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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.
Author: Anubhav Singh
Publisher: Packt Publishing Ltd
ISBN: 178961399X
Category : Computers
Languages : en
Pages : 372
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Book Description
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter Key FeaturesWork through projects covering mobile vision, style transfer, speech processing, and multimedia processingCover interesting deep learning solutions for mobileBuild your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook Description Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android. What you will learnCreate your own customized chatbot by extending the functionality of Google AssistantImprove learning accuracy with the help of features available on mobile devicesPerform visual recognition tasks using image processingUse augmented reality to generate captions for a camera feedAuthenticate users and create a mechanism to identify rare and suspicious user interactionsDevelop a chess engine based on deep reinforcement learningExplore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applicationsWho this book is for This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app’s user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.
Author: Francois Chollet
Publisher: Simon and Schuster
ISBN: 1638352046
Category : Computers
Languages : en
Pages : 597
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Book Description
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
