Speech Enhancement by Spectral Subtraction Based on Subspace Decomposition

Takahiro MURAKAMI; Tetsuya HOYA; Yoshihisa ISHIDA

doi:10.1093/ietfec/e88-a.3.690

Speech Enhancement by Spectral Subtraction Based on Subspace Decomposition

Takahiro MURAKAMI, Tetsuya HOYA, Yoshihisa ISHIDA

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This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.3 pp.690-701

Publication Date: 2005/03/01

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DOI: 10.1093/ietfec/e88-a.3.690

Type of Manuscript: PAPER

Category: Speech and Hearing

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Takahiro MURAKAMI, Tetsuya HOYA, Yoshihisa ISHIDA, "Speech Enhancement by Spectral Subtraction Based on Subspace Decomposition" in IEICE TRANSACTIONS on Fundamentals, vol. E88-A, no. 3, pp. 690-701, March 2005, doi: 10.1093/ietfec/e88-a.3.690.
Abstract: This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.3.690/_p

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@ARTICLE{e88-a_3_690,
author={Takahiro MURAKAMI, Tetsuya HOYA, Yoshihisa ISHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Speech Enhancement by Spectral Subtraction Based on Subspace Decomposition},
year={2005},
volume={E88-A},
number={3},
pages={690-701},
abstract={This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.},
keywords={},
doi={10.1093/ietfec/e88-a.3.690},
ISSN={},
month={March},}

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TY - JOUR
TI - Speech Enhancement by Spectral Subtraction Based on Subspace Decomposition
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 690
EP - 701
AU - Takahiro MURAKAMI
AU - Tetsuya HOYA
AU - Yoshihisa ISHIDA
PY - 2005
DO - 10.1093/ietfec/e88-a.3.690
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2005
AB - This paper presents a novel algorithm for spectral subtraction (SS). The method is derived from a relation between the spectrum obtained by the discrete Fourier transform (DFT) and that by a subspace decomposition method. By using the relation, it is shown that a noise reduction algorithm based on subspace decomposition is led to an SS method in which noise components in an observed signal are eliminated by subtracting variance of noise process in the frequency domain. Moreover, it is shown that the method can significantly reduce computational complexity in comparison with the method based on the standard subspace decomposition. In a similar manner to the conventional SS methods, our method also exploits the variance of noise process estimated from a preceding segment where speech is absent, whereas the noise is present. In order to more reliably detect such non-speech segments, a novel robust voice activity detector (VAD) is then proposed. The VAD utilizes the spread of eigenvalues of an autocorrelation matrix corresponding to the observed signal. Simulation results show that the proposed method yields an improved enhancement quality in comparison with the conventional SS based schemes.
ER -