MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments

Yang LIU; Shota MORITA; Masashi UNOKI

doi:10.1587/transfun.E99.A.560

MTF-Based Kalman Filtering with Linear Prediction for Power Envelope Restoration in Noisy Reverberant Environments

Yang LIU, Shota MORITA, Masashi UNOKI

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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

noisy reverberant speech, power envelope, modulation transfer function (MTF), linear prediction, Kalman filtering

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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

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@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},}

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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 -