IEICE TRANSACTIONS on Fundamentals
Robust F0 Estimation Based on Complex LPC Analysis for IRS Filtered Noisy Speech
Summary :
This paper proposes a novel robust fundamental frequency (F0) estimation algorithm based on complex-valued speech analysis for an analytic speech signal. Since analytic signal provides spectra only over positive frequencies, spectra can be accurately estimated in low frequencies. Consequently, it is considered that F0 estimation using the residual signal extracted by complex-valued speech analysis can perform better for F0 estimation than that for the residual signal extracted by conventional real-valued LPC analysis. In this paper, the autocorrelation function weighted by AMDF is adopted for the F0 estimation criterion and four signals; speech signal, analytic speech signal, LPC residual and complex LPC residual, are evaluated for the F0 estimation. Speech signals used in the experiments were an IRS filtered speech corrupted by adding white Gaussian noise or Pink noise whose noise levels are 10, 5, 0, -5 [dB]. The experimental results demonstrate that the proposed algorithm based on complex LPC residual can perform better than other methods in noisy environment.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.8 pp.1579-1586
- Publication Date
- 2007/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e90-a.8.1579
- Type of Manuscript
- Special Section PAPER (Special Section on Papers Selected from the 21st Symposium on Signal Processing)
- Category
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Keiichi FUNAKI, Tatsuhiko KINJO, "Robust F0 Estimation Based on Complex LPC Analysis for IRS Filtered Noisy Speech" in IEICE TRANSACTIONS on Fundamentals,
vol. E90-A, no. 8, pp. 1579-1586, August 2007, doi: 10.1093/ietfec/e90-a.8.1579.
Abstract: This paper proposes a novel robust fundamental frequency (F0) estimation algorithm based on complex-valued speech analysis for an analytic speech signal. Since analytic signal provides spectra only over positive frequencies, spectra can be accurately estimated in low frequencies. Consequently, it is considered that F0 estimation using the residual signal extracted by complex-valued speech analysis can perform better for F0 estimation than that for the residual signal extracted by conventional real-valued LPC analysis. In this paper, the autocorrelation function weighted by AMDF is adopted for the F0 estimation criterion and four signals; speech signal, analytic speech signal, LPC residual and complex LPC residual, are evaluated for the F0 estimation. Speech signals used in the experiments were an IRS filtered speech corrupted by adding white Gaussian noise or Pink noise whose noise levels are 10, 5, 0, -5 [dB]. The experimental results demonstrate that the proposed algorithm based on complex LPC residual can perform better than other methods in noisy environment.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.8.1579/_p
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@ARTICLE{e90-a_8_1579,
author={Keiichi FUNAKI, Tatsuhiko KINJO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Robust F0 Estimation Based on Complex LPC Analysis for IRS Filtered Noisy Speech},
year={2007},
volume={E90-A},
number={8},
pages={1579-1586},
abstract={This paper proposes a novel robust fundamental frequency (F0) estimation algorithm based on complex-valued speech analysis for an analytic speech signal. Since analytic signal provides spectra only over positive frequencies, spectra can be accurately estimated in low frequencies. Consequently, it is considered that F0 estimation using the residual signal extracted by complex-valued speech analysis can perform better for F0 estimation than that for the residual signal extracted by conventional real-valued LPC analysis. In this paper, the autocorrelation function weighted by AMDF is adopted for the F0 estimation criterion and four signals; speech signal, analytic speech signal, LPC residual and complex LPC residual, are evaluated for the F0 estimation. Speech signals used in the experiments were an IRS filtered speech corrupted by adding white Gaussian noise or Pink noise whose noise levels are 10, 5, 0, -5 [dB]. The experimental results demonstrate that the proposed algorithm based on complex LPC residual can perform better than other methods in noisy environment.},
keywords={},
doi={10.1093/ietfec/e90-a.8.1579},
ISSN={1745-1337},
month={August},}
Copy
TY - JOUR
TI - Robust F0 Estimation Based on Complex LPC Analysis for IRS Filtered Noisy Speech
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1579
EP - 1586
AU - Keiichi FUNAKI
AU - Tatsuhiko KINJO
PY - 2007
DO - 10.1093/ietfec/e90-a.8.1579
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2007
AB - This paper proposes a novel robust fundamental frequency (F0) estimation algorithm based on complex-valued speech analysis for an analytic speech signal. Since analytic signal provides spectra only over positive frequencies, spectra can be accurately estimated in low frequencies. Consequently, it is considered that F0 estimation using the residual signal extracted by complex-valued speech analysis can perform better for F0 estimation than that for the residual signal extracted by conventional real-valued LPC analysis. In this paper, the autocorrelation function weighted by AMDF is adopted for the F0 estimation criterion and four signals; speech signal, analytic speech signal, LPC residual and complex LPC residual, are evaluated for the F0 estimation. Speech signals used in the experiments were an IRS filtered speech corrupted by adding white Gaussian noise or Pink noise whose noise levels are 10, 5, 0, -5 [dB]. The experimental results demonstrate that the proposed algorithm based on complex LPC residual can perform better than other methods in noisy environment.
ER -
English
