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
- Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández (eds.).
- Cham : Springer, 2021.
- Physical description
- 1 online resource (501 pages)
- Lecture notes in computer science ; 12693.
- LNCS sublibrary. SL 1, Theoretical computer science and general issues.
- 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.
- (source: Nielsen Book Data)
- Publisher's 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.
(source: Nielsen Book Data)
- Evolutionary programming (Computer science) > Congresses.
- Natural computation > Congresses.
- Computer music > Congresses.
- Computer art > Congresses.
- Programmation évolutive > Congrès.
- Calcul naturel > Congrès.
- Computer art.
- Computer music.
- Evolutionary programming (Computer science)
- Natural computation.
- Publication date
- Title variation
- EvoMUSART 2021
- Lecture Notes in Computer Science ; 12693
- LNCS sublibrary, SL 1, Theoretical computer science and general issues
- Includes author index.
- 9783030729141 (electronic bk.)
- 3030729141 (electronic bk.)