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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
José M. Iñesta is a Professor in the Department of Software and Computing Systems at the Universidad de Alicante, Spain.
Darrell Conklin is a Professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country.
Rafael Ramírez-Melendez is Associate Professor in the Music Technology Group in the Department of Information and Communication Technologies at the Universidad Pompeu Fabra, Barcelona, Spain.
Thomas M. Fiore is Associate Professor of Mathematics at the University of Michigan-Dearborn, MI, USA.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Codice articolo 26375766270
Quantità: 1 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Codice articolo 370279201
Quantità: 1 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. Codice articolo 18375766260
Quantità: 1 disponibili
Da: Chiron Media, Wallingford, Regno Unito
Hardcover. Condizione: New. Codice articolo 6666-TNFPD-9780815377207
Quantità: 5 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 32319268
Quantità: 10 disponibili
Da: moluna, Greven, Germania
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 m. Codice articolo 595052614
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 32319268-n
Quantità: 10 disponibili
Da: Mispah books, Redhill, SURRE, Regno Unito
Hardcover. Condizione: New. New. book. Codice articolo ERICA75808153772075
Quantità: 1 disponibili
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
Hardback. Condizione: New. New copy - Usually dispatched within 4 working days. 429. Codice articolo B9780815377207
Quantità: 1 disponibili
Da: preigu, Osnabrück, Germania
Buch. Condizione: Neu. Machine Learning and Music Generation | José M. Iñesta (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2017 | CRC Press | EAN 9780815377207 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Codice articolo 133290915
Quantità: 5 disponibili