A Speech Enhancement Technique Using Kalman Filter with State Vector of Time-Frequency Patterns
Summary :
A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.4 pp.1027-1033
- Publication Date
- 2001/04/01
- Publicized
- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Section on Acoustic Signal Processing)
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Ryouichi NISHIMURA, Futoshi ASANO, Yoiti SUZUKI, Toshio SONE, "A Speech Enhancement Technique Using Kalman Filter with State Vector of Time-Frequency Patterns" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 4, pp. 1027-1033, April 2001, doi: .
Abstract: A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_4_1027/_p
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@ARTICLE{e84-a_4_1027,
author={Ryouichi NISHIMURA, Futoshi ASANO, Yoiti SUZUKI, Toshio SONE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Speech Enhancement Technique Using Kalman Filter with State Vector of Time-Frequency Patterns},
year={2001},
volume={E84-A},
number={4},
pages={1027-1033},
abstract={A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - A Speech Enhancement Technique Using Kalman Filter with State Vector of Time-Frequency Patterns
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1027
EP - 1033
AU - Ryouichi NISHIMURA
AU - Futoshi ASANO
AU - Yoiti SUZUKI
AU - Toshio SONE
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2001
AB - A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.
ER -
English
