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Artificial Intelligence in Music, Sound, Art and Design de Juan Romero

Artificial Intelligence in Music, Sound, Art and Design: Theoretical Computer Science and General Issues

Editat de Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández
en Limba Engleză Paperback – 2 apr 2021
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.
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Specificații

ISBN-13: 9783030729134
ISBN-10: 3030729133
Pagini: 508
Ilustrații: XIII, 492 p. 236 illus., 181 illus. in color.
Dimensiuni: 155 x 235 x 28 mm
Greutate: 0.76 kg
Ediția:1st edition 2021
Editura: Springer
Seria Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Cuprins

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-InitiativeLevel 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.