IEICE TRANSACTIONS on Fundamentals
MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments
Summary :
This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.2 pp.560-569
- Publication Date
- 2016/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.560
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Yang LIU
Japan Advanced Institute of Science and Technology
Shota MORITA
Japan Advanced Institute of Science and Technology
Masashi UNOKI
Japan Advanced Institute of Science and Technology
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Yang LIU, Shota MORITA, Masashi UNOKI, "MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 2, pp. 560-569, February 2016, doi: 10.1587/transfun.E99.A.560.
Abstract: This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.560/_p
Copy
@ARTICLE{e99-a_2_560,
author={Yang LIU, Shota MORITA, Masashi UNOKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments},
year={2016},
volume={E99-A},
number={2},
pages={560-569},
abstract={This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.},
keywords={},
doi={10.1587/transfun.E99.A.560},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 560
EP - 569
AU - Yang LIU
AU - Shota MORITA
AU - Masashi UNOKI
PY - 2016
DO - 10.1587/transfun.E99.A.560
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2016
AB - This paper proposes a method based on modulation transfer function (MTF) to restore the power envelope of noisy reverberant speech by using a Kalman filter with linear prediction (LP). Its advantage is that it can simultaneously suppress the effects of noise and reverberation by restoring the smeared MTF without measuring room impulse responses. This scheme has two processes: power envelope subtraction and power envelope inverse filtering. In the subtraction process, the statistical properties of observation noise and driving noise for power envelope are investigated for the criteria of the Kalman filter which requires noise to be white and Gaussian. Furthermore, LP coefficients drastically affect the Kalman filter performance, and a method is developed for deriving LP coefficients from noisy reverberant speech. In the dereverberation process, an inverse filtering method is applied to remove the effects of reverberation. Objective experiments were conducted under various noisy reverberant conditions to evaluate how well the proposed Kalman filtering method based on MTF improves the signal-to-error ratio (SER) and correlation between restored power envelopes compared with conventional methods. Results showed that the proposed Kalman filtering method based on MTF can improve SER and correlation more than conventional methods.
ER -
English
