Integrated Models of Signals and Background for an HMM/Neural Net Ocean Acoustic Events Classifier

Abstract

This paper investigates the use of Hidden Markov models (HMMs) for the classification and detection of ocean acoustic events in a nonstationary ocean background. A statistical formalism is described for integrating models for dynamic acoustic events and ocean background into a unified statistical framework. In this framework, both signal processes and background processes are modeled as HMMs, and signal classification is performed by obtaining the likelihood of a corrupted observation sequence through a combined state space of signal and background. Techniques are presented for estimating the acoustic event model parameters from training exemplars that are observed in these difficult background conditions. Finally, a novel neural network technique is proposed for the automatic learning of the nonlinear mechanism through which signal and background observations interact. Experimental results are presented.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA244889

Entities

People

  • R. C. Rose
  • W. Y. Huang

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acoustic Signals
  • Additives (Chemicals)
  • Algorithms
  • Automated Speech Recognition
  • Computational Science
  • Contour Integrals
  • Data Science
  • Decoding
  • Information Science
  • Integrals
  • Measurement
  • Neural Networks
  • Probability
  • Simulations
  • Standards
  • Statistical Algorithms
  • Training

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks
  • Space