title: Acoustic transient classification with a template correlation processor.
abstract: I present an architecture for acoustic pattern classification using trinary-trinary template correlation. In spite of its computational simplicity, the algorithm and architecture represent a method which greatly reduces bandwidth of the input, storage requirements of the classifier memory, and power consumption of the system without compromising classification accuracy. The linear system should be amenable to training using recently-developed methods such as Independent Component Analysis (ICA), and we predict that behavior will be qualitatively similar to that of structures in the auditory cortex.
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723-6099, USA.
authors.forename: R T
authors.initials: RT
authors.lastname: Edwards
mesh_terms: D000465:Algorithms; D000818:Animals; D001303:Auditory Cortex / Q000502:physiology; D001307:Auditory Perception; D003051:Cochlea / Q000502:physiology; D003202:Computers, Analog; D004867:Equipment Design; D008322:Mammals; D016571:Neural Networks, Computer