| Description |
1 online resource (501 pages) |
| Series |
Lecture Notes in Computer Science ; 12693 |
|
LNCS sublibrary, SL 1, Theoretical computer science and general issues |
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Lecture notes in computer science ; 12693.
|
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LNCS sublibrary. SL 1, Theoretical computer science and general issues.
|
| Contents |
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 |
| Summary |
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 |
| Notes |
Includes author index |
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Print version record |
| Subject |
Evolutionary programming (Computer science) -- Congresses
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Natural computation -- Congresses
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Computer music -- Congresses
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Computer art -- Congresses
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Computer art
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Computer music
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Evolutionary programming (Computer science)
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Natural computation
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Romero, Juan
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Martins, Tiago.
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Rodríguez-Fernández, Nereida
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EVOSTAR (Conference) (2021 : Online)
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| ISBN |
9783030729141 |
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3030729141 |
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