Crowd-Sourced Procedural Animation Optimisation: Comparing Desktop and VR Behaviour

Henshall, Gareth I.; Teahan, William J.; Cenydd, Llyr ap

doi:10.1109/cw.2017.52

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Several of the recommender system types described above attempt to use self-identified musical preferences expressed by participants, alongside their demographic information. Genre taxonomies derived from the semantic web, such as those of DBpedia, offer numerous musical categories in hierarchies (Schreiber, 2016), while others offer rather few parent/root genre similarities (Sturm, 2013b;Tzanetakis & Cook, 2002). These inconsistencies lead to misclassification and confusion (Sturm, 2013a) and poor content-based recommendation (Bogdanov, Porter, Urbano, & Schreiber, 2017;Sturm, 2013b).…”

Section: Music Genre and Feature Classificationmentioning

confidence: 99%

Encouraging Attention and Exploration in a Hybrid Recommender System for Libraries of Unfamiliar Music

Taylor

Dean

2019

Music & Science

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There are few studies of user interaction with music libraries comprising solely of unfamiliar music, despite such music being represented in national music information centre collections. We aim to develop a system that encourages exploration of such a library. This study investigates the influence of 69 users’ pre-existing musical genre and feature preferences on their ongoing continuous real-time psychological affect responses during listening and the acoustic features of the music on their liking and familiarity ratings for unfamiliar art music (the collection of the Australian Music Centre) during a sequential hybrid recommender-guided interaction. We successfully mitigated the unfavorable starting conditions (no prior item ratings or participants’ item choices) by using each participant’s pre-listening music preferences, translated into acoustic features and linked to item view count from the Australian Music Centre database, to choose their seed item. We found that first item liking/familiarity ratings were on average higher than the subsequent 15 items and comparable with the maximal values at the end of listeners’ sequential responses, showing acoustic features to be useful predictors of responses. We required users to give a continuous response indication of their perception of the affect expressed as they listened to 30-second excerpts of music, with our system successfully providing either a “similar” or “dissimilar” next item, according to—and confirming—the utility of the items’ acoustic features, but chosen from the affective responses of the preceding item. We also developed predictive statistical time series analysis models of liking and familiarity, using music preferences and preceding ratings. Our analyses suggest our users were at the starting low end of the commonly observed inverted-U relationship between exposure and both liking and perceived familiarity, which were closely related. Overall, our hybrid recommender worked well under extreme conditions, with 53 unique items from 100 chosen as “seed” items, suggesting future enhancement of our approach can productively encourage exploration of libraries of unfamiliar music.

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Section: Music Genre and Feature Classificationmentioning

confidence: 99%

Encouraging Attention and Exploration in a Hybrid Recommender System for Libraries of Unfamiliar Music

Taylor

Dean

2019

Music & Science

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“…We have previously conducted a similar experiment to the one described in this paper on a much simpler procedural animation system, consisting of seven parameters describing basic snake-like creatures [31,32]. In that experiment, we asked participants to rate randomly generated creatures on their physical attributes.…”

Section: A Dolphin Modelmentioning

confidence: 99%

Virtual reality’s effect on parameter optimisation for crowd-sourced procedural animation

2018

Self Cite

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Procedural animation systems are capable of synthesising lifelike organic motion automatically. However, due to extensive parameterisation, tuning these systems can be very difficult. Not only are there potentially hundreds of interlinked parameters, the resultant animation can be very subjective and the process is difficult to automate effectively. In this paper, we describe a crowd-sourced approach to procedural animation parameter optimisation using genetic algorithms. We test our approach by asking users to interactively rate a population of virtual dolphins to a prescribed behavioural criterion. Our results show that within a few generations a group of users can successfully tune the system towards a desired behaviour. Our secondary motivation is to investigate whether there are differences in animation and behavioural preference between observations made using a standard desktop monitor and those made in virtual reality (VR). We describe a study where users tuned two sets of dolphin animation systems in parallel, one using a normal monitor and another using an Oculus Rift. Our results indicate that being immersed in VR leads to some key differences in preferred behaviour.

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Scientific Knowledge Database to Support Cybersickness Detection and Prevention

França,

Amaral,

Rosa

et al. 2024

Lecture Notes in Computer Science

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Crowd-Sourced Procedural Animation Optimisation: Comparing Desktop and VR Behaviour

Cited by 4 publications

References 26 publications

Encouraging Attention and Exploration in a Hybrid Recommender System for Libraries of Unfamiliar Music

Encouraging Attention and Exploration in a Hybrid Recommender System for Libraries of Unfamiliar Music

Virtual reality’s effect on parameter optimisation for crowd-sourced procedural animation

Scientific Knowledge Database to Support Cybersickness Detection and Prevention

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