Intrinsic structure study of whale vocalizations

Published

Conference Paper

© 2016 IEEE. Whale vocalizations can be modeled as polynomial-phase signals, which are widely used in radar and sonar applications. Such signals lie on a nonlinear manifold parameterized by polynomial phase coefficients. In this paper, we apply manifold learning methods, in particular ISOMAP and Laplacian Eigenmap, to examine the underlying geometric structure of whale vocalizations. We can improve the classification accuracy by using the intrinsic structure of whale vocalizations. Our experiments on the DCLDE conference and MobySound data show that manifold learning methods such as ISOMAP and Laplacian eigenmap outperform linear dimension reduction methods such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS).

Full Text

Duke Authors

Cited Authors

  • Xian, Y; Sun, X; Liao, W; Zhang, Y; Nowacek, D; Nolte, L

Published Date

  • November 28, 2016

Published In

  • Oceans 2016 Mts/Ieee Monterey, Oce 2016

International Standard Book Number 13 (ISBN-13)

  • 9781509015375

Digital Object Identifier (DOI)

  • 10.1109/OCEANS.2016.7761101

Citation Source

  • Scopus