Auditory Displays and Assistive Technologies: the use of head movements by visually impaired individuals and their implementation in binaural interfaces

Visually impaired people rely upon audition for a variety of purposes, among these are the use of sound to identify the position of objects in their surrounding environment. This is limited not just to localising sound emitting objects, but also obstacles and environmental boundaries, thanks to their ability to extract information from reverberation and sound reflections- all of which can contribute to effective and safe navigation, as well as serving a function in certain assistive technologies thanks to the advent of binaural auditory virtual reality. It is known that head movements in the presence of sound elicit changes in the acoustical signals which arrive at each ear, and these changes can improve common auditory localisation problems in headphone-based auditory virtual reality, such as front-to-back reversals. The goal of the work presented here is to investigate whether the visually impaired naturally engage head movement to facilitate auditory perception and to what extent it may be applicable to the design of virtual auditory assistive technology. Three novel experiments are presented; a field study of head movement behaviour during navigation, a questionnaire assessing the self-reported use of head movement in auditory perception by visually impaired individuals (each comparing visually impaired and sighted participants) and an acoustical analysis of inter-aural differences and cross- correlations as a function of head angle and sound source distance. It is found that visually impaired people self-report using head movement for auditory distance perception. This is supported by head movements observed during the field study, whilst the acoustical analysis showed that interaural correlations for sound sources within 5m of the listener were reduced as head angle or distance to sound source were increased, and that interaural differences and correlations in reflected sound were generally lower than that of direct sound. Subsequently, relevant guidelines for designers of assistive auditory virtual reality are proposed

Enabling technologies for audio augmented reality systems

Audio augmented reality (AAR) refers to technology that embeds computer-generated auditory content into a user's real acoustic environment. An AAR system has specific requirements that set it apart from regular human--computer interfaces: an audio playback system to allow the simultaneous perception of real and virtual sounds; motion tracking to enable interactivity and location-awareness; the design and implementation of auditory display to deliver AAR content; and spatial rendering to display spatialised AAR content. This thesis presents a series of studies on enabling technologies to meet these requirements. A binaural headset with integrated microphones is assumed as the audio playback system, as it allows mobility and precise control over the ear input signals. Here, user position and orientation tracking methods are proposed that rely on speech signals recorded at the binaural headset microphones. To evaluate the proposed methods, the head orientations and positions of three conferees engaged in a discussion were tracked. The binaural microphones improved tracking performance substantially. The proposed methods are applicable to acoustic tracking with other forms of user-worn microphones. Results from a listening test investigating the effect of auditory display parameters on user performance are reported. The parameters studied were derived from the design choices to be made when implementing auditory display. The results indicate that users are able to detect a sound sample among distractors and estimate sample numerosity accurately with both speech and non-speech audio, if the samples are presented with adequate temporal separation. Whether or not samples were separated spatially had no effect on user performance. However, with spatially separated samples, users were able to detect a sample among distractors and simultaneously localise it. The results of this study are applicable to a variety of AAR applications that require conveying sample presence or numerosity. Spatial rendering is commonly implemented by convolving virtual sounds with head-related transfer functions (HRTFs). Here, a framework is proposed that interpolates HRTFs measured at arbitrary directions and distances. The framework employs Delaunay triangulation to group HRTFs into subsets suitable for interpolation and barycentric coordinates as interpolation weights. The proposed interpolation framework allows the realtime rendering of virtual sources in the near-field via HRTFs measured at various distances

Real-time Microphone Array Processing for Sound-field Analysis and Perceptually Motivated Reproduction

This thesis details real-time implementations of sound-field analysis and perceptually motivated reproduction methods for visualisation and auralisation purposes. For the former, various methods for visualising the relative distribution of sound energy from one point in space are investigated and contrasted; including a novel reformulation of the cross-pattern coherence (CroPaC) algorithm, which integrates a new side-lobe suppression technique. Whereas for auralisation applications, listening tests were conducted to compare ambisonics reproduction with a novel headphone formulation of the directional audio coding (DirAC) method. The results indicate that the side-lobe suppressed CroPaC method offers greater spatial selectivity in reverberant conditions compared with other popular approaches, and that the new DirAC formulation yields higher perceived spatial accuracy when compared to the ambisonics method

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Mind the (computational) gap

Despite many advances in both computational intelligence and computational neuroscience, it is clear that we have yet to achieve the full potential of nature inspired solutions from studying the human brain. Models of brain function have reached the stage where large-scale models of the brain have become possible, yet these tantalising computational structures cannot yet be applied to real-world problems because they lack the ability to be connected to real-world inputs or outputs. This paper introduces the notion of creating a computational hub that has the potential to link real sensory stimuli to higher cortical models. This is achieved through modelling subcortical structures, such as the superior colliculus, which have desirable computational principles, including rapid, multisensory and discriminative processing. We demonstrate some of these subcortical principles in a system that performs real-time speaker localisation using live video and audio, showing how such models may help us bridge the computational gap

VR/AR and hearing research: current examples and future challenges

A well-known issue in clinical audiology and hearing research is the level of abstraction of traditional experimental assessments and methods, which lack ecological validity and differ significantly from real-life experiences, often resulting in unreliable outcomes. Attempts to deal with this matter by, for example, performing experiments in real-life contexts, can be problematic due to the difficulty of accurately identifying control-specific parameters and events. Virtual and augmented reality (VR/AR) have the potential to provide dynamic and immersive audiovisual experiences that are at the same time realistic and highly controllable. Several successful attempts have been made to create and validate VR-based implementations of standard audiological and linguistic tests, as well as to design procedures and technologies to assess meaningful and ecologically-valid data. Similarly, new viewpoints on auditory perception have been provided by looking at hearing training and auditory sensory augmentation, aiming at improving perceptual skills in tasks such as speech understanding and sound-source localisation. In this contribution, we bring together researchers active in this domain. We briefly describe experiments they have designed, and jointly identify challenges that are still open and common approaches to tackle the

Towards Computer Understanding of Human Interactions

People meet in order to interact - disseminating information, making decisions, and creating new ideas. Automatic analysis of meetings is therefore important from two points of view: extracting the information they contain, and understanding human interaction processes. Based on this view, this article presents an approach in which relevant information content of a meeting is identified from a variety of audio and visual sensor inputs and statistical models of interacting people. We present a framework for computer observation and understanding of interacting people, and discuss particular tasks within this framework, issues in the meeting context, and particular algorithms that we have adopted. We also comment on current developments and the future challenges in automatic meeting analysis