Semi-automatic classification of birdsong elements using a linear support vector machine.

ABSTRACT: Birdsong provides a unique model for understanding the behavioral and neural bases underlying complex sequential behaviors. However, birdsong analyses require laborious effort to make the data quantitatively analyzable. The previous attempts had succeeded to provide some reduction of human efforts involved in birdsong segment classification. The present study was aimed to further reduce human efforts while increasing classification performance. In the current proposal, a linear-kernel support vector machine was employed to minimize the amount of human-generated label samples for reliable element classification in birdsong, and to enable the classifier to handle highly-dimensional acoustic features while avoiding the over-fitting problem. Bengalese finch's songs in which distinct elements (

SUBMITTER: Tachibana RO 

PROVIDER: S-EPMC3962446 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature