Grey Filtering and Its Application to Speech Enhancement

Cheng-Hsiung HSIEH

Grey Filtering and Its Application to Speech Enhancement

Cheng-Hsiung HSIEH

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In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNR_imp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.

Publication: IEICE TRANSACTIONS on Information Vol.E86-D No.3 pp.522-533

Publication Date: 2003/03/01

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Type of Manuscript: Special Section PAPER (Special Issue on Speech Information Processing)

Category: Robust Speech Recognition and Enhancement

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Cheng-Hsiung HSIEH, "Grey Filtering and Its Application to Speech Enhancement" in IEICE TRANSACTIONS on Information, vol. E86-D, no. 3, pp. 522-533, March 2003, doi: .
Abstract: In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNR_imp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e86-d_3_522/_p

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@ARTICLE{e86-d_3_522,
author={Cheng-Hsiung HSIEH, },
journal={IEICE TRANSACTIONS on Information},
title={Grey Filtering and Its Application to Speech Enhancement},
year={2003},
volume={E86-D},
number={3},
pages={522-533},
abstract={In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNR_imp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.},
keywords={},
doi={},
ISSN={},
month={March},}

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TY - JOUR
TI - Grey Filtering and Its Application to Speech Enhancement
T2 - IEICE TRANSACTIONS on Information
SP - 522
EP - 533
AU - Cheng-Hsiung HSIEH
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2003
AB - In this paper, a grey filtering approach based on GM(1,1) model is proposed. Then the grey filtering is applied to speech enhancement. The fundamental idea in the proposed grey filtering is to relate estimation error of GM(1,1) model to additive noise. The simulation results indicate that the additive noise can be estimated accurately by the proposed grey filtering approach with an appropriate scaling factor. Note that the spectral subtraction approach to speech enhancement is heavily dependent on the accuracy of statistics of additive noise and that the grey filtering is able to estimate additive noise appropriately. A magnitude spectral subtraction (MSS) approach for speech enhancement is proposed where the mechanism to determine the non-speech and speech portions is not required. Two examples are provided to justify the proposed MSS approach based on grey filtering. The simulation results show that the objective of speech enhancement has been achieved by the proposed MSS approach. Besides, the proposed MSS approach is compared with HFR-based approach in [4] and ZP approach in [5]. Simulation results indicate that in most of cases HFR-based and ZP approaches outperform the proposed MSS approach in SNR_imp. However, the proposed MSS approach has better subjective listening quality than HFR-based and ZP approaches.
ER -