This work assesses the feasibility of predicting emotional intensities for a given player in a testbed multiplayer game, using facial expression data collected from other players in the multiplayer group. Whilst there is significant literature on the utilisation of affect detection to build models of player experience, little research considers the additional data provided from other players in a multiplayer setting, despite the inherently shared experiences that they provide. A dataset describing 24 participants is collected, detailing ten levels of a testbed game, Colour Rush, with data collected describing facial expression activity and responses to the Discrete Emotions Questionnaire. The viability of modelling uncaptured player experiences is tested using artificial neural networks trained on facial expression data from target players, non-target players and a combination of both. Findings indicate that multiplayer data can be beneficial in the prediction of a target player's emotional responses, although this holds true only in a minority of cases, and for specific groups of players.

The Affective Audio Dataset (AAD) is a new and novel dataset of non-musical, non-anthropomorphic sounds intended for use in affective research. Sounds are annotated for their affective qualities by sets of human participants. The dataset was created in response to a lack of suitable datasets within the domain of audio emotion recognition. A total of 780 sounds are selected from the BBC Sounds Library. Participants are recruited online and asked to rate a subset of sounds based on how they make them feel. Each sound is rated for arousal and valence. It was found that while evenly distributed, there was bias towards the low-valence, high-arousal quadrant, and displayed a greater range of ratings in comparison to others. The AAD is compared with existing datasets to check its consistency and validity, with differences in data collection methods and intended use-cases highlighted. Using a subset of the data, the online ratings were validated against an in-person data collection experiment with findings strongly correlating. The AAD is used to train a basic affect-prediction model and results are discussed. Uses of this dataset include, human-emotion research, cultural studies, other affect-based research, and industry use such as audio post-production, gaming, and user-interface design.

Classifying players into well defined groups can be useful when designing games and gamified systems, with many models relating to player or personality ‘type’. The Unified Model of Personality and Play Styles groups together many player and personality taxonomies, but whilst similarities have been noted in previous work, the overlap between models has not been analysed ahead of its use. This study provides evidence both for and against aspects of the Unified Model, with model agreeability assessed through comparison of participant classifications. Results show that representations of types related by the Unified Model do correlate significantly greater than types unrelated by the model, but do so with only weak-to-moderate correlation coefficients. Ranking classifications leads to results better mapping to the Unified Model, but also reduces the overall strength of correlations between types. The Unified Model is therefore considered fit for purpose as an explanatory tool, but without additional study should be used with caution in further use cases.

Abstract Foley artistry is an essential part of the audio post-production process for film, television, games, and animation. By extension, it is as crucial in emergent media such as virtual, mixed, and augmented reality. Footsteps are a core activity that a Foley artist must undertake and convey information about the characters and environment presented on-screen. This study sought to identify if characteristics of age, gender, weight, health, and confidence could be conveyed, using sounds created by a professional Foley artist, in three different 3D humanoid models, following a single walk cycle. An experiment was conducted with human participants (n=100) and found that Foley manipulations could convey all the intended characteristics with varying degrees of contextual success. It was shown that the abstract 3D models were capable of communicating characteristics of age, gender, and weight. A discussion of the literature and inspection of related audio features with the Foley clips suggest signal parameters of frequency, envelope, and novelty may be a subset of markers of those perceived characteristics. The findings are relevant to researchers and practitioners in linear and interactive media and demonstrate mechanisms by which Foley can contribute useful information and concepts about on-screen characters.

The esports industry has seen enormous growth in popularity. With increased viewership and revenue, further investment has been made to improve professional players’ competitive strength. The modern esports team is a hierarchical business fuelled by investors and sponsorship. This paper is focused on the professional competitions in League of Legends esports. In existing real-world sports such as football or baseball, there is great attention paid to statistic driven analysis of the competition, and these stats are used to quantify player and team performance. These statistics hold significant value for competitive improvement, the gambling industry, and market influence within the esports industry. This paper presents an analysis of data and metrics gathered from professional games during 2020 in several League of Legends international competitions. The objective was to build a predictive model through the combination of existing data analysis and machine learning that can rate team and player performance. The best performing model was able to correctly predict 67% of 306 games. Results indicate that while it is possible to predict the outcome of a competitive League of Legends game, to do so with a higher degree of accuracy would require substantially more data and contextual information.

Auditory alerts are an essential part of many multi-modal interaction scenarios, particularly in safety and mission critical settings, such as hospitals and transportation. A variety of strategies can be employed in the design of auditory alerts, often orienting manipulation of volume and pitch parameters. However, manipulations by applying a Doppler effect are under-investigated. A perceptual listening test is conducted (n = 100) using multiple alert sounds that are subjected to a variety of volume, pitch, and Doppler manipulations, with the unaltered sounds serving as a benchmark. Applying a mixed methods approach consisting of inferential statistics and thematic analysis, it is found that decreases in volume and a Doppler simulation of a sound moving away reduce importance and urgency, increase safety, are harder to detect, and are perceived as being more distant in perceptions of auditory alerts. Further, increases in volume and a Doppler simulation of a sound approaching are effective in communicating safety, whilst pitch manipulations were much less effective. Further work is required to provide wider, ecologically valid, verification of these findings, particularly as to how listener detection of Doppler and volume manipulations can be improved.

Abstract The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’srcorrelation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.

This paper investigates the relationship between audio models for Virtual Reality (VR) video with respect to the senses of immersion and realism that each model delivers. Mono, Stereo, 5.1 Surround Sound, and a Virtual Spatialised Position configuration was developed for testing in a VR music video and evaluated with a user study. Participants experienced the VR video with these differing audio models as accompaniment a total of four times. Qualitative and quantitative data were recorded to evaluate user experience. The results indicate that no statistical significance was present between the four models in relation to immersion or realism, suggesting that complex audio renderings are not always necessary for effective user experience.

The growth of the creative industries has been a national trend in the UK over the last 5 to 10 years, despite global trends of economic downturn, and has been mirrored on the international stage. A distinguishing feature of the creative sector is its make-up of practitioners from a broad spectrum of disciplines. In addition, creative processes often benefit from the collaboration of partners. Established models and challenges of creativity are assessed and contextualized against the contemporaneous creative industries, which feature multi-disciplinary teams, supported by current technology. Crowd-accelerated developments and creative collaboration via social media are having a transformative effect on the creation, distribution, and exhibition of creative works. They are also having an impact on traditional art and design processes. Multi-user interaction enables location-based art works to be transformed into new kinds of interactive and dynamic experiences for global viewers. Current opportunities, issues and challenges that arise are discussed, and a number of questions are identified for further discussion.

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