Comparison of Linear Prediction Models for Audio Signals
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with...
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2009-03-01
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Series: | EURASIP Journal on Audio, Speech, and Music Processing |
Online Access: | http://dx.doi.org/10.1155/2008/706935 |
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doaj-0dd912ae235b4852985e7993170f265d2020-11-25T02:44:07ZengSpringerOpenEURASIP Journal on Audio, Speech, and Music Processing1687-47141687-47222009-03-01200810.1155/2008/706935Comparison of Linear Prediction Models for Audio SignalsWhile linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling. http://dx.doi.org/10.1155/2008/706935 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
title |
Comparison of Linear Prediction Models for Audio Signals |
spellingShingle |
Comparison of Linear Prediction Models for Audio Signals EURASIP Journal on Audio, Speech, and Music Processing |
title_short |
Comparison of Linear Prediction Models for Audio Signals |
title_full |
Comparison of Linear Prediction Models for Audio Signals |
title_fullStr |
Comparison of Linear Prediction Models for Audio Signals |
title_full_unstemmed |
Comparison of Linear Prediction Models for Audio Signals |
title_sort |
comparison of linear prediction models for audio signals |
publisher |
SpringerOpen |
series |
EURASIP Journal on Audio, Speech, and Music Processing |
issn |
1687-4714 1687-4722 |
publishDate |
2009-03-01 |
description |
While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling. |
url |
http://dx.doi.org/10.1155/2008/706935 |
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1724767345661968384 |