Spatial Perception and Cognition in Multichannel Audio for Electroacoustic Music

Listeners experience electroacoustic music as full of significance and meaning, and they experience spatiality as one of the factors contributing to its meaningfulness. If we want to understand spatiality in electroacoustic music, we must understand how the listener’s mental processes give rise to the experience of meaning. In electroacoustic music as in everyday life, these mental processes unite the peripheral auditory system with human spatial cognition. In the discussion that follows we consider a range of the listener’s mental processes relating space and meaning from the perceptual attributes of spatial imagery to the spatial reference frames for places and navigation. When considering multichannel loudspeaker systems in particular, an important part of the discussion is focused on the distinctive and idiomatic ways in which this particular mode of sound production contributes to and situates meaning. These idiosyncrasies include the phenomenon of image dispersion, the important consequences of the precedence effect and the influence of source characteristics on spatial imagery. These are discussed in close relation to the practicalities of artistic practice and to the potential for artistic meaning experienced by the listener.

Inter-Frame Contextual Modelling For Visual Speech Recognition

In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter-Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system.