Accession Number:

ADA277500

Title:

Recognizing Successive Dolphin Echoes with an Integrator Gateway Network

Descriptive Note:

Professional paper

Corporate Author:

NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA

Report Date:

1993-11-01

Pagination or Media Count:

8.0

Abstract:

This paper describes a novel network architecture developed to classify multiple successive echoes from targets ensonified by a dolphin echolocating in a naturalistic environment. The inputs to the network were spectral vectors of the echo plus one unit representing the start of each scan. This network combined information from successive echoes from the same target and reset between scans of different targets. The network was trained on a small subset 4 of the total set of available echoes 1,335. Depending on the measure used to assess it, the network correctly classified between 90 and 93 of all echo trains. In contrast, a standard backpropagation network with the same number of units and variable connections performed with only about 63 accuracy in classifying echo trains. The integration model seems to provide a better account of the dolphins performance than a decision model that does not combine information from multiple echoes. Artificial neural networks ANN, Echolocation, Gateway-integrator neural network

Subject Categories:

  • Biological Oceanography
  • Acoustic Detection and Detectors
  • Acoustics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE