A Speed-Invariant Temporal Feature Detector

A Speed-Invariant Temporal Feature Detector

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by Paul Cisek Department , Paul E. Cisek , Michael A. Cohen

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Datum	Value	Source
TITLE	A Speed-Invariant Temporal Feature Detector	SVM HeaderParse 0.1
AUTHOR NAME	Paul Cisek Department	user correction - Legacy Corrections
AUTHOR NAME	Paul E. Cisek	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Cognitive and Neural Systems; Boston University	user correction - Legacy Corrections
AUTHOR ADDR	111 Cummington Street, Boston, MA 02215	user correction - Legacy Corrections
AUTHOR NAME	Michael A. Cohen	user correction - Legacy Corrections
AUTHOR AFFIL	Center for Adaptive Systems; Boston University	user correction - Legacy Corrections
AUTHOR ADDR	111 Cummington Street, Boston, MA 02215	user correction - Legacy Corrections
ABSTRACT	Temporal pattern classification normally operates upon vectors whose components are time-delayed samples, be they samples of spectral, cepstral, or LPC coefficients over time. Such a spatial representation of temporal patterns is very sensitive to speed variations, and requires computationally expensive time-alignment techniques such as Dynamic Time Warping in order to compare inputs to exemplars. This paper proposes a speed-invariant representation of temporal patterns using Taylor series expansion. A vector composed of successive time-derivative samples of the temporal signal is unique to the shape of the function in a region around the sampling point. Simple manipulations on such a "Taylor vector" yield a speed-invariant form. The degree of speed invariance can be controlled parametrically while retaining sensitivity to the direction of presentation. Simulations demonstrate that learning and recall of temporal pattterns coded using this representation is accurate and do...	user correction - Legacy Corrections
CITATIONS	7 found	ParsCit 1.0