Deep Learning Techniques for Music GenerationКНИГИ » ПРОГРАММИНГ
Название: Deep Learning Techniques for Music Generation Автор: Jean-Pierre Briot, Gaetan Hadjere Издательство: Springer Год: 2019 Страниц: 303 Язык: английский Формат: pdf (true) Размер: 12.1 MB
This book is a survey and analysis of how Deep Learning (DL) 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.
There is no consensual definition for Deep Learning. It is a repertoire of Machine Learning (ML) techniques, based on artificial neural networks. The key aspect and common ground is the term deep. This means that there are multiple layers processing multiple hierarchical levels of abstractions, which are automatically extracted from data. Thus a deep architecture can manage and decompose complex representations in terms of simpler representations. The technical foundation is mostly artificial neural networks, as we will see in Chapter 5, with many extensions, such as: convolutional networks, recurrent networks, autoencoders, and restricted Boltzmann machines. For more information about the history and various facets of deep learning, see, e.g., a recent comprehensive book on the domain. Driving applications of deep learning are traditional machine learning tasks: classification (for instance, identification of images) and prediction (for instance, of the weather) and also more recent ones such as translation. But a growing area of application of deep learning techniques is the generation of content. Content can be of various kinds: images, text and music, the latter being the focus of our analysis. The motivation is in using now widely available various corpora to automatically learn musical styles and to generate new musical content based on this.
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.
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Deep Learning Techniques for Music Generation 
