The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'analyse de prédiction linéaire (LP) est couramment utilisée dans le traitement de la parole. LP est basé sur le modèle auto-régressif (AR) et estime le paramètre du modèle AR à partir de signaux avec l2-optimisation des normes. Récemment, l’estimation clairsemée a retenu l’attention car elle peut extraire des caractéristiques significatives du Big Data. L’estimation parcimonie est réalisée par l1 or l0-optimisation ou régularisation des normes. Méthodes d'analyse Sparse LP basées sur l1-une optimisation des normes a été proposée. Puisque l’excitation de la parole n’est pas gaussienne blanche, une estimation LP clairsemée peut estimer un paramètre plus précis que l’estimation conventionnelle. l2-LP basé sur les normes. Il s’agit d’analyses invariantes dans le temps et à valeur réelle. Nous avons étudié l'analyse Time-Varying Complex AR (TV-CAR) pour un signal analytique et avons évalué les performances en matière de traitement de la parole. Les méthodes TV-CAR sont l2-méthodes normatives. Dans cet article, nous proposons l'analyse TV-CAR clairsemée basée sur le LASSO adaptatif (opérateur de retrait et de sélection le moins absolu) qui est l1-régularisation des normes et évaluation des performances sur F0 estimation de la parole par IRAPT (Instantaneous RAPT). Les résultats expérimentaux montrent que les méthodes TV-CAR clairsemées fonctionnent mieux pour un niveau élevé de bruit rose additif.
Keiichi FUNAKI
University of the Ryukyus
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Keiichi FUNAKI, "Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 12, pp. 1910-1914, December 2019, doi: 10.1587/transfun.E102.A.1910.
Abstract: Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1910/_p
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@ARTICLE{e102-a_12_1910,
author={Keiichi FUNAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO},
year={2019},
volume={E102-A},
number={12},
pages={1910-1914},
abstract={Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.},
keywords={},
doi={10.1587/transfun.E102.A.1910},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1910
EP - 1914
AU - Keiichi FUNAKI
PY - 2019
DO - 10.1587/transfun.E102.A.1910
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2019
AB - Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l2-norm optimization. Recently, sparse estimation is paid attention since it can extract significant features from big data. The sparse estimation is realized by l1 or l0-norm optimization or regularization. Sparse LP analysis methods based on l1-norm optimization have been proposed. Since excitation of speech is not white Gaussian, a sparse LP estimation can estimate more accurate parameter than the conventional l2-norm based LP. These are time-invariant and real-valued analysis. We have been studied Time-Varying Complex AR (TV-CAR) analysis for an analytic signal and have evaluated the performance on speech processing. The TV-CAR methods are l2-norm methods. In this paper, we propose the sparse TV-CAR analysis based on adaptive LASSO (Least absolute shrinkage and selection operator) that is l1-norm regularization and evaluate the performance on F0 estimation of speech using IRAPT (Instantaneous RAPT). The experimental results show that the sparse TV-CAR methods perform better for a high level of additive Pink noise.
ER -