Simon Moncrieff

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We develop an algorithm for the detection and classification of <i>affective</i> sound events underscored by specific patterns of sound energy dynamics. We relate the portrayal of these events to proposed high level <i>affect</i> or emotional coloring of the events. In this paper, four possible characteristic sound energy events are identified that convey(More)
We present a method for foreground/background separation of audio using a background modelling technique. The technique models the background in an online, unsupervised, and adaptive fashion, and is designed for application to long term surveillance and monitoring problems. The background is determined using a statistical method to model the states of the(More)
In this paper, we study the sound tracks in films and their indexical semiotic usage by developing a classification system that detects complex sound scenes and their constituent sound events in cinema. We investigate two main issues in this paper: Determination of what constitutes the presence of a high level sound scene and inferences about the thematic(More)
Surveillance applications in private environments such as smart houses require a privacy management policy if such systems are to be accepted by the occupants of the environment. This is due to the invasive nature of surveillance, and the private nature of the home. In this article, we propose a framework for dynamically altering the privacy policy applied(More)
We examine localised sound energy patterns, or events, that we associate with high level affect experienced with films. The study of sound energy events in conjunction with their intended affect enable the analysis of film at a higher conceptual level, such as genre. The various affect/emotional responses we investigate in this paper are brought about by(More)
In this paper we present preliminary work implementing dynamic privacy in public surveillance. The aim is to maximise the privacy of those under surveillance, while giving an observer access to sufficient information to perform their duties. As these aspects are in conflict, a dynamic approach to privacy is required to balance the systempsilas purpose with(More)
We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are determined using multi-modal sensor data. The anxiety(More)
A smart house can be regarded as a surveillance environment in which the person being observed carries out activities that range from intimate to more public. What can be observed depends on the activity, the person observing (e.g. a carer) and policy. In assisted living smart house environments, a single privacy policy, applied throughout, would be either(More)