LEADER 05652nam 22007935 450 001 9910484699303321 005 20230329235539.0 010 $a3-030-72914-1 024 7 $a10.1007/978-3-030-72914-1 035 $a(CKB)4100000011867180 035 $a(MiAaPQ)EBC6532698 035 $a(Au-PeEL)EBL6532698 035 $a(OCoLC)1245663408 035 $a(DE-He213)978-3-030-72914-1 035 $a(PPN)255289375 035 $a(EXLCZ)994100000011867180 100 $a20210401d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence in Music, Sound, Art and Design $e10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, April 7?9, 2021, Proceedings /$fedited by Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (501 pages) $cillustrations 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v12693 311 $a3-030-72913-3 320 $aIncludes bibliographical references and index. 327 $aSculpture 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. 330 $aThis book constitutes the refereed proceedings of the 10th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021, held as part of Evo* 2021, as Virtual Event, in April 2021, co-located with the Evo* 2021 events, EvoCOP, EvoApplications, and EuroGP. The 24 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 66 submissions. They cover a wide range of topics and application areas, including generative approaches to music and visual art, deep learning, and architecture. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v12693 606 $aComputer science 606 $aEducation?Data processing 606 $aMachine learning 606 $aImage processing?Digital techniques 606 $aComputer vision 606 $aArtificial intelligence 606 $aSoftware engineering 606 $aTheory of Computation 606 $aComputers and Education 606 $aMachine Learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aArtificial Intelligence 606 $aSoftware Engineering 615 0$aComputer science. 615 0$aEducation?Data processing. 615 0$aMachine learning. 615 0$aImage processing?Digital techniques. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aSoftware engineering. 615 14$aTheory of Computation. 615 24$aComputers and Education. 615 24$aMachine Learning. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aArtificial Intelligence. 615 24$aSoftware Engineering. 676 $a005.11 702 $aMartins$b Tiago 702 $aRodri?guez-Ferna?ndez$b Nereida 702 $aRomero$b Juan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484699303321 996 $aArtificial Intelligence in Music, Sound, Art and Design$92257587 997 $aUNINA