Artificial Intelligence in Music, Sound, Art and Design

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.

Inhalt

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.