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The real world presents our sensory systems with a continuous stream of undifferentiated information. Segmentation of this stream at event boundaries is necessary for object identification and feature extraction. Here, we investigate the neural dynamics of event segmentation in entire musical symphonies under natural listening conditions. We isolated(More)
Raster scanning is a technique for generating or recording a video image by means of a line-by-line sweep, tantamount to a data mapping scheme between one and two dimensional spaces. While this geometric structure has been widely used on many data transmission and storage systems as well as most video displaying and capturing devices, its application to(More)
SonART is a flexible, multipurpose multimedia environment that allows for networked collaborative interaction with applications for art, science and industry. In this paper we describe the integration of image and audio that SonART enables. An arbitrary number of layered canvases, each with independent control of opacity, RGB values, etc., can transmit or(More)
We explore the potential for and implications of musical (or proto-musical) social interaction and collaboration using currently available technologies embedded into mobile phones. The dynamics of this particular brand of social intercourse and the emergence of an associated aesthetic is described. The clichéd concept of a global village is made a vibrant(More)
In our previous work we proposed a theory of cognition of tonal music based on control of expectations and created a model to test the theory using a hierarchical sequential neural network. The net learns metered and rhythmecized functional tonal harmonic progressions allowing us to measure uctuations in the degree of realized expectation (DRE). Preliminary(More)
We describe a model of music cognition based on uctuations in the degree of realized expectation (DRE) in which we employ a neural network which receives representations of standard and anomalous chord progressions derived from opening periods of piano sonatas by Mozart and Haydn. In order to account for essential metric information we incorporate(More)
The human ability to recognize, identify and compare sounds based on their approximation of particular vowels provides an intuitive, easily learned representation for complex data. We describe implementations of vocal tract models specifically designed for sonification purposes. The models described are based on classical models including Klatt[1] and(More)