Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO

Keiichi FUNAKI

doi:10.1587/transfun.E102.A.1910

Sparse Time-Varying Complex AR (TV-CAR) Speech Analysis Based on Adaptive LASSO

Keiichi FUNAKI

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.12 pp.1910-1914

Publication Date: 2019/12/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E102.A.1910

Type of Manuscript: Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)

Category: Speech and Hearing

Authors

Keiichi FUNAKI
University of the Ryukyus

Keyword

sparse LP, time-varying analysis, complex analysis, analytic signal, adaptive LASSO, l₁-norm regularization, F₀ estimation

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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 -