Mexico

MuSyFI is a system that tries to model an inspirational computational creative process. It uses images as source of inspiration and begins by implementing a possible translation between visual and musical features. Three different musical artifacts are generated: an automatic version, a co-created version, and a genetic version. The automatic version maps features from the image into musical features nondeterministically; the co-created version adds harmony lines manually composed by us to the automatic version; finally, the genetic version applies a genetic algorithm to a mixed population of automatic and co-created artifacts. In this Demo, we present the three different versions for four of the six evaluated images in our surveys

ICCC&#39;21

MuSyFI - Music Synthesis From Images

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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### **Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!**

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Computational creativity is an interdisciplinary endeavor that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, social sciences and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:

* to construct a program or computer capable of showing some level of creativity
* to better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans
* to design programs that can enhance human creativity without necessarily being creative themselves

<br>The ICCC conference series organized by the [**Association for Computational Creativity**](https://computationalcreativity.net/) since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.



To access the Twelfth International Conference on Computational Creativity, ICCC’21, you need to register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the Twelfth International Conference on Computational Creativity, ICCC’21 conference website. 

[Register now](https://computationalcreativity.net/iccc21/registration/)

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ICCC'21

Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!

Portmanteaus are a type of neologism combining two source words, for example brunch, and are popular for naming all kinds of phenomena. While coming up with suitable portmanteaus is a difficult creative endeavor, several portmanteau generators already exist for assistance in this process. When using these systems, it is often hard to find out which of the generated portmanteau is likely to be the best, and consequently also hard to automatically compare the quality of different portmanteau generators. In this paper, we create a model that can rank portmanteaus for two given source words, which thus aims to help find the best portmanteau to help further improve portmanteau generators. Our model first uses XGBoost trained on unlabeled generated outputs and existing portmanteaus to learn to rank portmanteaus and shows that this already greatly improves the performance of the initial generator. By ranking outputs of a state-of-the-art generator and a new simple portmanteau generator, we show by validating its quality in a human evaluation that the ranker can help visually identify the better generator, thus providing an alternative to only calculating real portmanteau generation frequency. Additionally, we find that this first model performs almost as well as a model trained on more fine-grained human-labeled portmanteaus. This indicates that just using generated and real portmanteaus is enough to create a ranker that can in turn improve the quality of the initial generator, and could additionally be of use in comparing different portmanteau generators.

Learning to Rank Generated Portmanteaus

SOVIA, an interactive system that endows Claude Monet’s art with responsive auditory experiences. SOVIA uses computer vision trained on Monet’s artwork to take the user ``into the painting.'' When the user interacts with a digital version of Monet's landscapes, their mouse positions are mapped to sounds that artistically represent the objects that the user is currently exploring in the art. These interactive musical journeys have the potential to make classical art more captivating for modern audiences.

SOVIA: Sonification of Visual Interactive Art

We describe recent work on the Aris system that creates and verifies new formal specifications for pre-existing source code. We describe Aris in terms of the abductive reasoning system that suggest possible specifications and then uses an existing deductive verifier to evaluate these creations. This paper focuses on the abduction system that creates new formal specifications by leveraging a small set of inspiring artefacts to augment a subset of candidate problems. This employs knowledge graphs to represent the raw data (i.e., source code), discovering latent similarities between graphs using a graph-matching process. Results are presented for the C# programming language with novel creations and its sister language called Code Contracts. We outline ampliative creativity, whereby newly created artefacts drive subsequent creative episodes beyond the initially perceived limitations. We also outline some recent work towards transferring specifications between the C# and Java programming languages.

Creating new Program Proofs by Combining Abductive and Deductive Reasoning

In this short paper, we reflect on the long quest for intelligence and creativity of computing machinery as well as the suitability for prevailing machine learning techniques to be used in creative tasks. We believe that modularization and multi-layered structures are among essential ingredients constituting creative minds and may greatly benefit machines on handling creative tasks. For proof of concept, we select musical composition, particularly, Species Counterpoint, as the task, adopt a recently proposed computational framework designed for investigating creativity and the creative process, and present an implementation capable of producing the outcomes that exhibit the desired effect.

In the Name of Creativity: En Route to Inspiring Machines

In this technical demonstration paper we document the use of a Generative Adversarial Network (GAN) based casual creator game for systematic assessment of human creativity. We discuss some of the challenges in designing GAN-based casual creators, specifically focusing on how to identify and select appropriate parts of the latent vector space - images, in our case.

CREA.blender: a casual creator for creativity assessment

We present a proof-of-concept prototype aimed at providing a subjectivity touch to the production of creative artifacts. The underlying idea is to have a system in which the development of the artifact occurs concurrently with the dynamic formation of a personal identity, thus enriching the overall perception of creativity.

Dynamic Creation of Points of View

This work presents CoPE, a dance education framework for AI systems with three key components: Composition, Performance, and Evaluation. By analyzing nowadays dance education, we suggest how to incorporate principles from the dance community into AI-based dance generation systems by aiming for more realistic pieces of art.

Composition, Performance and Evaluation: A Dance Education Framework for AI Systems

In recent years computer simulations have proven to be useful in the study of the origin and evolution of com- munication. In this paper, we present a system that is able to evolve image-based communication protocols to transmit information. We trained an encoder and a de- coder in an architecture similar to a model of commu- nication where the generator transforms a message into an image and the decoder tries to reconstruct the origi- nal message. This way the two networks are stimulated to work together to establish some type of communica- tion. We used the GloVe word to vector dataset to generate images for concepts and took advantage of its linear properties to generate new concepts. We analyse the results by comparing images of similar concepts and demonstrated that our system is capable of creating sim- ilar images for related concepts, distancing itself from different concepts.

Towards a Visual Language Using Neural Networks

We present a Computational Creativity system based on Conceptual Blending theory. To obtain diverse output -- in the form of blend spaces -- it allows a trade-off in the optimisation process according to multiple criteria. It is handled by a high performance Multiple Objective Evolutionary Algorithm supporting a large Knowledge Base, large input spaces, a high number of mappings as well of frames, all in the range of millions of elements in their structures. Some of CB's Optimality Principles were adapted into objectives as well as new ideas we decided to investigate. Initial experiments allows us to conclude that the system exhibits a form of creativity and its output is capable of depict a simple drawing or a short story.

A Heavy Duty Computational Model of Conceptual Blending Using Multiple Objective Optimisation

Wölfflin Affective Generative Analysis (WAGA) as an approach to understand and analyze the progress of machine-generated artworks in contrast to real art and their connection to our human artistic heritage, and how they extend the shape of art history. Specifically, we studied the machine-generated art after integrating creativity losses in the state-of-the-art generative models e.g., StyleGAN v1 and v2. We denote these models as Style Creative Adversarial Networks v1 and v2; in short, StyleCAN v1 and v2. We contrasted the learned representation between real and generated artworks through correlation analysis between constructed emotion (collected through Amazon MTurk) and Heinrich Wölfflin (1846-1945)'s principles of art history. Analogous to the recent ArtEmis dataset, we collected constructed emotions and explanations on generated art instead of real art to study the contrast. To enable Wölfflin Affective Generative Analysis, we collected 45,000 annotations on 1800 artworks; 1000 real and 800 generated. Our analysis shows a correlation exists between the Wölfflin principles and the emotions.

MuSyFI - Music Synthesis From Images

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Learning to Rank Generated Portmanteaus

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MuSyFI - Music Synthesis From Images

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Next from ICCC'21

Learning to Rank Generated Portmanteaus

Similar lecture

ReadTwice: Reading Very Large Documents with Memories

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads