On Creativity and Open-Endedness

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALife, focusing specifically on the concept of Open-Endedness (OE); the primary goal of CC is to endow artificial systems with creativity, and ALife has dedicated much research effort into studying and synthesizing OE and artificial innovation. However, despite the close proximity of these concepts, their use so far remains confined to their respective communities, and their relationship is largely unclear. We provide historical context for research in both domains, and review the limited work connecting research on creativity and OE explicitly. We then highlight specific questions to be considered, with the eventual goals of (i) decreasing conceptual ambiguity by highlighting similarities and differences between the concepts of OE, (ii) identifying synergy effects of a research agenda that encompasses both OE and creativity, and (iii) establishing a dialogue between ALife and CC research.

Reef Elegy: An Auditory Display of Hawaii's 2019 Coral Bleaching Data

This paper describes an auditory display of Hawaii's 2019 coral bleaching data via means of spatial audio and parameter mapping methods. Selected data fields spanning 78 days are mapped to sound surrogates of coral reefs' natural soundscapes, which are progressively altered in their constituent elements as the corresponding coral locations undergo bleaching. For some of these elements, this process outlines a trajectory from a dense to a sparser, reduced soundscape, while for others it translates moving away from harmonic tones and towards complex spectra. This experiment is accompanied by a short evaluation study to contextualize it in an established aesthetic perspective space and to probe its potential for public engagement in the discourse around climate change.

Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data

Amid growing environmental concerns, interactive displays of data constitute an important tool for exploring and understanding the impact of climate change on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a query-based sonification model of Tokyo's air temperature from 1876 to 2021. The system uses a recurrent neural network architecture known as LSTM with attention trained on a small dataset of Japanese melodies and conditioned upon said atmospheric data. After describing the model's implementation, a brief comparative illustration of the musical results is presented, along with a discussion on how the exposed hyper-parameters can promote active and non-linear exploration of the data.

Modeling Baroque Two-Part Counterpoint with Neural Machine Translation

We propose a system for contrapuntal music generation based on a Neural Machine Translation (NMT) paradigm. We consider Baroque counterpoint and are interested in modeling the interaction between any two given parts as a mapping between a given source material and an appropriate target material. Like in translation, the former imposes some constraints on the latter, but doesn't define it completely. We collate and edit a bespoke dataset of Baroque pieces, use it to train an attention-based neural network model, and evaluate the generated output via BLEU score and musicological analysis. We show that our model is able to respond with some idiomatic trademarks, such as imitation and appropriate rhythmic offset, although it falls short of having learned stylistically correct contrapuntal motion (e.g., avoidance of parallel fifths) or stricter imitative rules, such as canon.