English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Plant Classification from Bat-Like Echolocation Signals

Yovel, Y., Franz, M., Stilz, P., & Schnitzler, H.-U. (2008). Plant Classification from Bat-Like Echolocation Signals. PLoS Computational Biology, 4(3 ), 1-13. doi:10.1371/journal.pcbi.1000032.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CA29-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3169-0
Genre: Journal Article

Files

show Files

Creators

show
hide
 Creators:
Yovel, Y, Author
Franz, MO1, 2, Author              
Stilz, P, Author
Schnitzler, H-U, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects.

Details

show
hide
Language(s):
 Dates: 2008-03
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pcbi.1000032
eDoc: e1000032
BibTex Citekey: 5089
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: PLoS Computational Biology
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 4 (3 ) Sequence Number: - Start / End Page: 1 - 13 Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1