A learning approach to hierarchical feature selection and aggregation for audio classification

Authors:
Paul Ruvolo;Ian Fasel;Javier R. Movellan
Affiliations:
Machine Perception Laboratory, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA;University of Arizona, Department of Computer Science, P.O. Box 210077, Tucson, AZ 85721-0077, USA;Machine Perception Laboratory, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
Venue:
Pattern Recognition Letters
Year:
2010

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Abstract

Audio classification typically involves feeding a fixed set of low-level features to a machine learning method, then performing feature aggregation before or after learning. Instead, we jointly learn a selection and hierarchical temporal aggregation of features, achieving significant performance gains.