Passive acoustic monitoring (PAM) is being adopted in a range of contexts. Emerging methods facilitate analysis of large-scale data sets, but ecological interpretation of acoustic indices is not straightforward. In addition, the technical and logistical requirements of using emerging AI methods for big data mean that conservation actors increasingly adopt third-party analysis solutions. We argue that these compounding factors undermine robust ecological inference, clouding insight and decision making. To address this, we present echo-dash, an accessible, interactive dashboard that facilitates rapid, interactive exploration of spatiotemporal soundscape data by conservation actors. Developed through participatory design, echo-dash is built on the simple premise that the potential of PAM can best be realised by keeping human ecological knowledge in the analysis loop. Five key functions to facilitate analysis and interpretation of PAM data were identified and implemented: 1) Calculating soundscape descriptors and probability of species presence; 2) Checking data integrity; 3) Exploring data interactively and in spatiotemporal, environmental contexts; 4) Filtering data by extreme weather, outliers or clusters; 5) Exporting data subsets, plots and code to generate plots. By supporting integration of human, situated ecological knowledge with large scale spatiotemporal data sets, echo-dash bolsters PAM’s potential to transform ecological monitoring for applied and fundamental ecoacoustics.

Artificial creativity is often applied in the production of artefacts and ideas for a human audience. However, as a creative force that is not bound to human experiences, it can act as a way of approaching nonhuman creative forces from a new perspective. This paper develops a concept of endemic machines to describe a process of engaging the creativity of an ecosystem through a machine that adapts with that ecosystem. A case study detailing the design and testing of an endemic machine called the Rowdy Krause helps to ground the concept of endemic machines in practice.

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user's situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans' situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.

Acoustic indices are valuable tools for measuring and tracking changes in biodiversity. However, the method used to collect acoustic index data can be made more effective by recent developments in electronics. The current process requires recording high-quality audio in the field and computing acoustic indices in the lab. This produces vast quantities of raw audio data, which limits the time that sensors can spend in the field and complicates data processing and analysis. Additionally, most field audio recorders are unable to log the full range of contextual environmental data that would help explain short-term variations. In this paper, we present the BioAcoustic Index Tool, a smart acoustic index and environmental sensor. The BioAcoustic Index Tool computes acoustic indices as audio is captured, storing only the index information, and logs temperature, humidity, and light levels. The sensor was able to operate completely autonomously for the entire five-month duration of the field study. In that time, it recorded over 4000 measurements of acoustic complexity and diversity all while producing the same amount of data that would be used to record 3 minutes of raw audio. These factors make the BioAcoustic Index Tool well-suited for large-scale, long-term acoustic biodiversity monitoring.

Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects - specifically people - in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing People-Art testing dataset. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.

Adaptation is an important capability in a fast-changing world. What factors allow an animal population to adapt to external changes in their environments? What effects do those changes have on the...

Artificial life simulations are an important tool in the study of ecological phenomena that can be difficult to examine directly in natural environments.Recent work has established the soundscape as an ecologically important resource and it has been proposed that the differentiation of animal vocalizations within a soundscape is driven by the imperative of intraspecies communication.The experiments in this paper test that hypothesis in a simulated soundscape in order to verify the feasibility of intraspecies communication as a driver of acoustic niche differentiation.The impact of intraspecies communication is found to be a significant factor in the division of a soundscape's frequency spectrum when compared to simulations where the need to identify signals from conspecifics does not drive the evolution of signalling.The method of simulating the effects of interspecies interactions on the soundscape is positioned as a tool for developing artificial life agents that can inhabit and interact with physical ecosystems and soundscapes.

This paper delineates the conceptual outcomes from a two-week intensive cross-disciplinary conversation between an art historian, an interaction designer, and an artist/engineer. With the aim of applying the concept of technogenesis to an exploration of sound as material for art and design, we consider sound as a material force within an ecosystem. Through this lens, sound produced by either life- or technological-forms allows us to consider the ecological impact and potential meanings of generated sound. Drawing on biosemiotics, we propose that the co-evolution of sound, technology, and environments, what we call eco-technogenesis, demands relational, and thus ethical, thinking. The rowdy krause, an autonomous sonic agent, designed by Kadish to identify and inhabit an acoustic niche within an ecosystem, serves as a case study for thinking through eco-technogenesis.

Sorting data into groups and clusters is one of the fundamental tasks of artificially intelligent systems. Classical clustering algorithms rely on heuristic (k-nearest neighbours) or statistical methods (k-means, fuzzy c-means) to derive clusters and these have performed well. Neural networks have also been used in clustering data, but researchers have only recently begun to adopt the strategy of having neural networks directly determine the cluster membership of an input datum. This paper presents a novel strategy, employing NeuroEvolution of Augmenting Topologies to produce an evoltionary neural network capable of directly clustering unlabelled inputs. It establishes the use of cluster validity metrics in a fitness function to train the neural network.

Wicked environmental problems are complex, ill-defined and constantly shifting. Analytical methods alone are typically under-equipped to address these problems. High-low tech is a craftmaking and design practice that encourages makers to merge high- and low-tech materials, processes and cultures. We explore how high-low tech practices can create new ways of understanding wicked environmental problems and lead practitioners towards new ways of approaching them. Through five examples of high-low tech practices, we explore the qualities of these practices-an embrace of complexity, a transdisciplinary approach to work and a maker ethos-that make high-low tech practices particularly well suited to addressing wicked environmental problems.

Inorganisms is a workshop that engages participants in a process of creation through reflective practice as a way of building and understanding complexity. It models a design process aimed at addressing large-scale global issues that asks designers to create solutions on a local scale, while forming interconnections to neighbouring places and designers. The workshop takes a pragmatic approach to learning through design and experience in order to come to a better understanding of complex, emergent systems. Participants are asked to design and create an inorganic organism from provided building blocks. Each inorganism communicates with other inorganisms, ultimately creating an emergent ecosystem of inorganisms – a small-scale model of local, connected solutions to global problems. No prior electronics experience is required, so experts and novices alike are encouraged to participate.

The Empathy Machine is an interactive installation that augments a visitor's empathic sense during a social conversation. Empathy is a key component of interpersonal interactions that is often neglected by modern communication technologies. This system uses facial expression recognition to identify the emotional state of a user's conversation partner. It algorithmically generates emotional music to match the expressive state of the partner and plays the music to the user in a non-disruptive manner. The result is an augmentation of the user's emotional response to the emotional expression of their partner.

The Empathy Machine is an interactive installation that augments a visitor’s empathic sense during a social conversation. Empathy is a key component of interpersonal interactions that is often neglected by modern communication technologies. This system uses facial expression recognition to identify the emotional state of a user’s conversation partner. It algorithmically generates emotional music to match the expressive state of the partner and plays the music to the user in a non-disruptive manner. The result is an augmentation of the user’s emotional response to the emotional expression of their partner.

Empathy is a key component of interpersonal interactions that is often neglected by modern communication technologies. This paper presents the prototyping and initial testing of a device that enhances a person’s empathic sense. The system uses facial expression recognition to identify the emotional state of a user’s conversation partner. It algorithmically generates music in real-time to match the expressive state of the partner and plays the music to the user in a nondestructive manner. User testing indicates that the system can reliably generate music corresponding to the emotions of anger, happiness fear and sadness and that the presence of emotional music augments the emotional response generated by visual cues.

Empathy is a key component of interpersonal interactions that is often neglected by modern communication technologies. This paper presents the prototyping and initial testing of a device that enhances a person’s empathic sense. The system uses facial expression recognition to identify the emotional state of a user’s conversation partner. It algorithmically generates music in real-time to match the expressive state of the partner and plays the music to the user in a nondestructive manner. User testing indicates that the system can reliably generate music corresponding to the emotions of anger, happiness fear and sadness and that the presence of emotional music augments the emotional response generated by visual cues.

In this paper we discuss the Order of Passions, generative media installation that visualizes dynamism, disturbance and unity within the diverse set of human facial expressions that together create a collective and emergent polyphonic portrait of Canada. We discuss critical compositional, technical and meaning making strategies for the creation of this generative artwork. The discussion is positioned from the perspective of artist-creators dealing with computational media as a medium for both, creative production and presentation of the artwork. We describe the tools and processes that were used and developed to support the creation of