Sound event recognition through expectancy-based evaluation ofsignal-driven hypotheses

Authors:
J. D. Krijnders;M. E. Niessen;T. C. Andringa
Affiliations:
Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands;Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands;Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

A recognition system for environmental sounds is presented. Signal-driven classification is performed by applying machine-learning techniques on features extracted from a cochleogram. These possibly unreliable classifications are improved by creating expectancies of sound events based on context information.