An analog neural net performing multidimensional maximum entropy spectral estimation

Xinhua Zhuang, Hyonam Joo, Seho Oh, Yunxin Zhao, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review

Abstract

A general algorithm is presented for computationally efficient multidimensional maximum entropy (ME) spectral estimation. The estimator is equivalent to an analog neural net that is governed by an energy function that measures the degree of constraint satisfaction, i.e., correlation-matching property. The multidimensional ME (MDME) spectral-estimation problem is defined. ME spectral estimation is formulated as an initial-value problem. The MDME spectral estimators, or algorithms, for solving the initial-value problem are developed. The neural net algorithm is derived, and simulated experiments with 1-D or 2-D signals are conducted. In each, the assumed true spectrum is given and autocorrelations at a number of horizontally and vertically equally spaced lags are calculated from the Fourier transform of the true spectrum. The overall results indicate very good performance in estimating ME spectra, even in cases where there are few autocorrelation measurements.

Original languageEnglish (US)
Pages (from-to)2077-2080
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - Dec 1 1990
Externally publishedYes
Event1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA
Duration: Apr 3 1990Apr 6 1990

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'An analog neural net performing multidimensional maximum entropy spectral estimation'. Together they form a unique fingerprint.

Cite this