Hey folks! 👋

Are you working on research in Artificial Intelligence for creative purposes like visual art, music generation, sound synthesis, architecture, poetry, video, or design? EvoMUSART 2025 offers a great opportunity to present your work!

EvoMUSART 2025 focuses on showcasing applications of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, and other computational techniques in creative tasks.

📍 Location: Trieste, Italy
📅 Date: 23-25 April 2025
🗓️ Paper Submission Deadline: 1 November 2024

Visit the link for details and submission guidelines:
https://www.evostar.org/2025/evomusart

https://preview.redd.it/742hknbm2rrd1.png?width=4167&format=png&auto=webp&s=63601df9a36838644bba0f6ade9bdb927bab2061

I apologize if this is not the right place, but I would like to create some lessons for generative music for my 7th grade computer science class using Scratch. I was wondering if anyone has tried doing this with much success or could lead me down the right path to follow. Thank you ahead of time.

Hi all, I've been working on this Kontakt instrument where you can load a sample and then fold it into all kinds of different patterns using a bunch of step sequencers.

Have a look and let me know what you think.
https://youtu.be/FG0LvOwnF5g?si=_zTjW4Ik-RSzdduS

Thank you,
Tak

Dear colleagues,

Juan Romero, Penousal Machado and Colin Johnson will publish a Special Issue associated with EvoMUSART on "Complex Systems in Aesthetics, Creativity and Arts" and it would be a pleasure if you sent an extension of your contribution.

Journal: Complexity (ISSN 1076-2787)
JCR Journal with Impact factor: 1.7 (Q2)
Deadline for manuscript submissions: 18 October 2024

Special Issue URL: https://onlinelibrary.wiley.com/doi/toc/10.1155/8503.si.941484
Instructions for authors: https://onlinelibrary.wiley.com/page/journal/8503/homepage/author-guidelines

One of the main - possibly unattainable - challenges of computational arts is to build algorithms that evaluate properties such as novelty, creativity, and aesthetic properties of artistic artifacts or representations. Approaches in this regard have often been based on information-theoretic ideas. For example, ideas relating mathematical notions of form and balance to beauty date to antiquity. In the 20th century, attempts were made to develop aesthetic measures based on the ideas of balance between order and complexity. In recent years, these ideas have been formalized into the idea that aesthetic engagement occurs when work is on the "edge of chaos," between excessive order and excessive disorder, formalizing it through notions such as the Gini coefficient and Shannon entropy, and links between cognitive theories of Bayesian brain and free energy minimization with aesthetic theories. These ideas have been used both to understand human behavior and to build creative systems.

The use of artificial intelligence and complex systems for the development of artistic systems is an exciting and relevant area of research. In recent years, there has been an enormous interest in the application of these techniques in fields such as visual art and music generation, analysis and performance, sound synthesis, architecture, video, poetry, design, game content generation, and other creative endeavors.

This Special Issue invites original research and review articles which will focus on both the use of complexity ideas and artificial intelligence methods to analyze and evaluate aesthetic properties and to drive systems that generate aesthetically appealing artifacts, including: music, sound, images, animation, design, architectural plans, choreography, poetry, text, jokes, etc.

Potential topics include but are not limited to the following:

• Computational aesthetics
• Formalising the ideas of aesthetics using ideas from entropy and information theory
• Computational creativity
• Artificial Intelligence in art, design, architecture, music, and games
• Information Theory in art, design, architecture, music, and games
• Complex systems in art, music ,and design
• Evolutionary art and music
• Deep ;learning models to art and video creation
• Artificial life in arts
• Swarm art
• Pattern recognition and aesthetics
• Cellular automata in architecture
• Generative AI

Dr. Penousal Machado
Dr. Colin Johnson
Dr. Iria Santos

Guest Editors (EvoMUSART 2025)

These variations of the famous Soviet-era folk song, with a jazz-like progression towards the original theme, were created by changing the tonal properties of the theme using the Tonamic method. Apart from the added drums, the output score is not modified.

https://youtu.be/l6eFtdL_jXk

Please find attached a tool for sonification of integers sequences in form of a score:

https://musescore1983.pythonanywhere.com/

Here is a demo with the beginning of Moonlight Sonata, part 3 and a favourite integer sequence of mine: Abstract Moonlight Sonata 3. This tool works like this: It takes as input a score in the form of a midi and then, depending on the sequence, runs back and forth on the score and creates a variation. The minimum of the sequence corresponds roughly to the beginning, while the maximum corresponds to the end of the score. Other sequences for sonification might be found here: OEIS.

A basic L-system, written in Javascript, converting A to ABA, and B to BBB (the standard Cantor dust). I then take generation N and, on the same stave, pair it with generations N+1, N+2, and N+3. Each A token is then mapped to a specific note, according its generation.

(Since my video won't up,I have link it :(

https://youtu.be/Dmo0hygzeqE

NetWorks is a music-generating algorithm, based on complex systems science, that seeks to tap into the ceaseless creativity, and organic coherence, found in nature through fine-tuning the connectivity of networks, which channels how information flows through them, and the rules that transform the information as it interacts via their nodes.

Constraints on the connections and interactions between the parts of systems are central to their coherence. Alicia Juarrero in her book, Context Changes Everything writes: “Coherence-making by constraints takes place in physical and biological complex systems small and large, from Bénard cells to human organizations and institutions, from family units to entire cultures. Entities and events in economic and ecosystems are defined by such covarying relations generated by enabling constraints.”

In NetWorks, the transformation of information via the nodes is extremely simple, nodes send and receive simple values (negative and positive integers) that are added/subtracted together. 

Michael Levin, in his groundbreaking work on developmental bioelectricity, points out the important ability for cells to coarse grain their inputs. Cells track and respond to voltage and, as a general rule, are not concerned with the details, specifically, the individual ions, ion channels or molecules, that contributed to their voltage. It is the voltage patterns across cells which control cellular differentiation during morphogenesis and ontogeny.

In discussing the role of the observer, Stepen Wolfram points out the importance of equivalence in human thought and technology. He uses gas molecules and a piston as an example: the huge number of possible configurations of the gas is not important so long as they are equivalent in determining pressure. All that matters is the aggregate of all the molecular impacts. Equivalence is a key aspect on how we as observers make sense of the world, in that many different configurations of systems contribute to their aggregate features that we recognize while we, like our cells, can ignore most of the underlying details. 

Similarly, in the NetWork algorithm, nodes aggregate their inputs which are feedback into the network through their links. It is the network’s unfolding pattern of values that are sonified.  

The pieces in NetWorks 11: Unfamiliar Order consist of eight interacting voices. Voices can interact such that, for example, the depth of vibrato performed by one voice can influence the timbral characteristics and movement through 3D (ambisonic) space of a note played by another voice. The covarying relationship between musical attributes result in expressive context dependent performances. 

Headphone listening is recommended as the piece was mixed using ambisonic techniques.

https://shawnbell.bandcamp.com/album/unfamiliar-order