Adaptive predictors in cascade form to analyse superimposed exponential signals with time-varying parameters

Julio Vargas, Steve McLaughlin

Research output: Contribution to journalArticle

Abstract

In this paper adaptive predictors structured in cascade form are used to analyse superimposed exponential signals with time-varying parameters. On-line optimisation of the filters parameters is achieved using gradient descent techniques focused on the direct estimation of instantaneous information-bearing parameters (damping and frequency). Examples of the performance of the algorithm are given for real-valued multi-component synthetic signals with different schemes of frequency modulation in clean and noisy environments. The performance of the algorithm is also evaluated in the task of tracking the formants of a voiced segment of speech. (C) 2001 Elsevier Science BY. All rights reserved.

Original languageEnglish
Pages (from-to)2223-2233
Number of pages11
JournalSignal Processing
Volume81
Issue number10
DOIs
Publication statusPublished - Oct 2001

Cite this

@article{a45eff1a1e8b4f9e95e12b9c039179a5,
title = "Adaptive predictors in cascade form to analyse superimposed exponential signals with time-varying parameters",
abstract = "In this paper adaptive predictors structured in cascade form are used to analyse superimposed exponential signals with time-varying parameters. On-line optimisation of the filters parameters is achieved using gradient descent techniques focused on the direct estimation of instantaneous information-bearing parameters (damping and frequency). Examples of the performance of the algorithm are given for real-valued multi-component synthetic signals with different schemes of frequency modulation in clean and noisy environments. The performance of the algorithm is also evaluated in the task of tracking the formants of a voiced segment of speech. (C) 2001 Elsevier Science BY. All rights reserved.",
author = "Julio Vargas and Steve McLaughlin",
year = "2001",
month = "10",
doi = "10.1016/S0165-1684(01)00099-8",
language = "English",
volume = "81",
pages = "2223--2233",
journal = "Signal Processing",
issn = "0165-1684",
publisher = "Elsevier",
number = "10",

}

Adaptive predictors in cascade form to analyse superimposed exponential signals with time-varying parameters. / Vargas, Julio; McLaughlin, Steve.

In: Signal Processing, Vol. 81, No. 10, 10.2001, p. 2223-2233.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Adaptive predictors in cascade form to analyse superimposed exponential signals with time-varying parameters

AU - Vargas, Julio

AU - McLaughlin, Steve

PY - 2001/10

Y1 - 2001/10

N2 - In this paper adaptive predictors structured in cascade form are used to analyse superimposed exponential signals with time-varying parameters. On-line optimisation of the filters parameters is achieved using gradient descent techniques focused on the direct estimation of instantaneous information-bearing parameters (damping and frequency). Examples of the performance of the algorithm are given for real-valued multi-component synthetic signals with different schemes of frequency modulation in clean and noisy environments. The performance of the algorithm is also evaluated in the task of tracking the formants of a voiced segment of speech. (C) 2001 Elsevier Science BY. All rights reserved.

AB - In this paper adaptive predictors structured in cascade form are used to analyse superimposed exponential signals with time-varying parameters. On-line optimisation of the filters parameters is achieved using gradient descent techniques focused on the direct estimation of instantaneous information-bearing parameters (damping and frequency). Examples of the performance of the algorithm are given for real-valued multi-component synthetic signals with different schemes of frequency modulation in clean and noisy environments. The performance of the algorithm is also evaluated in the task of tracking the formants of a voiced segment of speech. (C) 2001 Elsevier Science BY. All rights reserved.

U2 - 10.1016/S0165-1684(01)00099-8

DO - 10.1016/S0165-1684(01)00099-8

M3 - Article

VL - 81

SP - 2223

EP - 2233

JO - Signal Processing

JF - Signal Processing

SN - 0165-1684

IS - 10

ER -