Vai al contenuto principale della pagina
Titolo: | Artificial Intelligence in Music, Sound, Art and Design [[electronic resource] ] : 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings / / edited by Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández |
Pubblicazione: | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
Edizione: | 1st ed. 2021. |
Descrizione fisica: | 1 online resource (501 pages) : illustrations |
Disciplina: | 005.11 |
Soggetto topico: | Computer science |
Education—Data processing | |
Machine learning | |
Image processing—Digital techniques | |
Computer vision | |
Artificial intelligence | |
Software engineering | |
Theory of Computation | |
Computers and Education | |
Machine Learning | |
Computer Imaging, Vision, Pattern Recognition and Graphics | |
Artificial Intelligence | |
Software Engineering | |
Persona (resp. second.): | MartinsTiago |
Rodríguez-FernándezNereida | |
RomeroJuan | |
Nota di bibliografia: | Includes bibliographical references and index. |
Nota di contenuto: | Sculpture Inspired Musical Composition, One Possible Approach -- Network Bending: Expressive Manipulation of Deep Generative Models -- SyVMO: Synchronous Variable Markov Oracle for Modeling and Predicting Multi-Part Musical Structures -- Identification of Pure Painting Pigment Using Machine Learning Algorithms -- Evolving Neural Style Transfer Blends -- Evolving Image Enhancement Pipelines -- Genre Recognition from Symbolic Music with CNNs -- Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks -- Interactive, Efficient and Creative Image Generation Using Compositional Pattern-Producing Networks -- Aesthetic Evaluation of Cellular Automata Configurations Using Spatial Complexity and Kolmogorov Complexity -- Auralization of Three-Dimensional Cellular Automata -- Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction -- Convolutional Generative Adversarial Network, via Transfer Learning, for Traditional Scottish Music Generation -- The Enigma of Complexity -- SerumRNN: Step by Step Audio VST Effect Programming -- Parameter Tuning for Wavelet-Based Sound Event Detection Using Neural Networks -- Raga Recognition in Indian Classical Music Using Deep Learning -- The Simulated Emergence of Chord Function -- Incremental Evolution of Stylized Images -- Dissecting Neural Networks Filter Responses for Artistic Style Transfer -- A Fusion of Deep and Shallow Learning to Predict Genres Based on Instrument and Timbre Features -- A Multi-Objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation -- Exploring the Effect of Sampling Strategy on Movement Generation with Generative Neural Networks -- "A Good Algorithm Does Not Steal - It Imitates": The Originality Report as a Means of Measuring when a Music Generation Algorithm Copies too Much -- From Music to Image - A Computational Creativity Approach -- “What is human?” A Turing Test for Artistic Creativity -- Mixed-Initiative Level Design with RL Brush -- Creating a Digital Mirror of Creative Practice -- An Application for Evolutionary Music Composition Using Autoencoders -- A Swarm Grammar-Based Approach to Virtual World Generation -- Co-Creative Drawing with One-Shot Generative Models. |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Artificial Intelligence in Music, Sound, Art and Design |
ISBN: | 3-030-72914-1 |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 996464386803316 |
Lo trovi qui: | Univ. di Salerno |
Opac: | Controlla la disponibilità qui |