Spatial linear predictive coding and its error matching for signal classification

Authors:
Tuan D. Pham
Affiliations:
Bioinformatics Applications Research Centre and School of Mathematics, Physics and Information Technology, James Cook University, Townsville, QLD, Australia
Venue:
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2006

Citing 3
Cited 0

Fundamentals of speech recognition

Fundamentals of speech recognition
Digital Signal Processing Using MATLAB: A BookWare Companion Problems Book

Digital Signal Processing Using MATLAB: A BookWare Companion Problems Book
Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mathematical analysis of the behavior of general dynamic systems based on linear prediction plays an essential role in many fields of science and engineering concerning the processing and representation of complex signals. This paper addresses the parameter estimation of the all-pole model of the linear predictive coding in the sense that the signal has both deterministic and random properties. Estimate of the model variance error is used as a basis for the derivation of a spatial distortion measure which can be used for matching spectral patterns.