Noisy speech enhancement using discrete cosine transform

This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing noise embedded in a speech signal. The derivation of the Minimum Mean Square Error (MMSE) filter based on the statistical modell...

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Published in	Speech communication Vol. 24; no. 3; pp. 249 - 257
Main Authors	Soon, Ing Yann, Koh, Soo Ngee, Yeo, Chai Kiat
Format	Journal Article
Language	English
Published	Amsterdam Elsevier B.V 01.06.1998 Elsevier
Subjects	Applied sciences Discrete cosine transform (DCT) Exact sciences and technology Information, signal and communications theory MMSE amplitude estimation Noise removal Signal processing Speech enhancement Speech processing Telecommunications and information theory Speech enhancement Noise removal MMSE amplitude estimation Discrete cosine transform (DCT) Spectral data Speech analysis Filtering Gaussian noise Noise reduction Speech recognition White noise Speech processing Cosine transform Mean square error
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Abstract	This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing noise embedded in a speech signal. The derivation of the Minimum Mean Square Error (MMSE) filter based on the statistical modelling of the DCT coefficients is shown. Also shown is the derivation of an over-attenuation factor based on the fact that speech energy is not always present in the noisy signal at all times or in all coefficients. This over-attenuation factor is useful in suppressing any musical residual noise which may be present. The proposed methods are evaluated against the noise reduction filter proposed by Y. Ephraim and D. Malah (1984), using both Gaussian distributed white noise as well as recorded fan noise, with favourable results. Cet article illustre les avantages apportés par l'utilisation de la Transformation Cosinus Discrète (DCT) par rapport à celle de la Transformée de Fourier Discrète (DFT) standard, pour le débruitage de la parole bruitée. On montre comment dériver un filtre MMSE à partir de la modélisation statistique des coefficients DCT. On montre également comment dériver un facteur de sur-atténuation basé sur le fait que, dans les signaux bruités, l'énergie de la parole n'est pas toujours présente à chaque instant ni dans chaque coefficient. Ce facteur de sur-atténuation est utile pour supprimer tout bruit résiduel musical. Les méthods proposées ont été évaluées favorablement par rapport du filtre de réduction de bruit proposé par Ephraim et Malah (1994), en utilisant tant du bruit blanc guassien que du bruit de ventilateur enregistré
AbstractList	The advantage of using the discrete cosine transform (DCT) over the standard discrete Fourier transform (DFT) for the purpose of removing noise embedded in a speech signal is illustrated. The derivation of the minimum mean square error filter based on the statistical modelling of the DCT coefficients is shown, as is the derivation of an over-attenuation factor based on the fact that speech energy is not present in the noisy signal at all times or in all coefficients. This over-attenuation factor is useful in suppressing any musical residual noise that may be present. The proposed methods are evaluated against the noise reduction filter proposed by Y. Ephraim & D. Malah (1984), using both Gaussian distributed white noise as well as recorded fan noise, with favorable results. 2 Tables, 8 Figures, 16 References. Adapted from the source document This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing noise embedded in a speech signal. The derivation of the Minimum Mean Square Error (MMSE) filter based on the statistical modelling of the DCT coefficients is shown. Also shown is the derivation of an over-attenuation factor based on the fact that speech energy is not always present in the noisy signal at all times or in all coefficients. This over-attenuation factor is useful in suppressing any musical residual noise which may be present. The proposed methods are evaluated against the noise reduction filter proposed by Y. Ephraim and D. Malah (1984), using both Gaussian distributed white noise as well as recorded fan noise, with favourable results. Cet article illustre les avantages apportés par l'utilisation de la Transformation Cosinus Discrète (DCT) par rapport à celle de la Transformée de Fourier Discrète (DFT) standard, pour le débruitage de la parole bruitée. On montre comment dériver un filtre MMSE à partir de la modélisation statistique des coefficients DCT. On montre également comment dériver un facteur de sur-atténuation basé sur le fait que, dans les signaux bruités, l'énergie de la parole n'est pas toujours présente à chaque instant ni dans chaque coefficient. Ce facteur de sur-atténuation est utile pour supprimer tout bruit résiduel musical. Les méthods proposées ont été évaluées favorablement par rapport du filtre de réduction de bruit proposé par Ephraim et Malah (1994), en utilisant tant du bruit blanc guassien que du bruit de ventilateur enregistré
Author	Koh, Soo Ngee Soon, Ing Yann Yeo, Chai Kiat
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Cites_doi	10.1109/89.397090 10.1109/TCOM.1978.1094144 10.1109/T-C.1974.223784 10.1109/TASSP.1984.1164453 10.1109/ICASSP.1979.1170788 10.1109/TASSP.1979.1163209 10.1109/ICASSP.1996.543199 10.1016/0165-1684(85)90002-7 10.1109/TASSP.1977.1162974 10.1109/TASSP.1980.1163353 10.1109/TASSP.1976.1162870 10.1109/89.279283 10.1109/TASSP.1980.1163394 10.21437/ICSLP.1990-295 10.1109/TCOM.1977.1093941
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Keywords	Speech enhancement Noise removal MMSE amplitude estimation Discrete cosine transform (DCT) Spectral data Speech analysis Filtering Gaussian noise Noise reduction Speech recognition White noise Speech processing Cosine transform Mean square error
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References_xml	– volume: 3 start-page: 251 year: 1995 end-page: 266 ident: BIB10 article-title: A signal subspace approach for speech enhancement publication-title: IEEE Trans. Speech and Audio Process. contributor: fullname: Malah – volume: 8 start-page: 387 year: 1985 end-page: 400 ident: BIB12 article-title: Noise suppression by spectral magnitude estimation – Mechanism and theoretical limits publication-title: Signal Processing contributor: fullname: Vary – volume: 3 start-page: 251 year: 1995 ident: 10.1016/S0167-6393(98)00019-3_BIB10 article-title: A signal subspace approach for speech enhancement publication-title: IEEE Trans. Speech and Audio Process. doi: 10.1109/89.397090 contributor: fullname: Ephraim – ident: 10.1016/S0167-6393(98)00019-3_BIB14 doi: 10.1109/TCOM.1978.1094144 – ident: 10.1016/S0167-6393(98)00019-3_BIB7 – ident: 10.1016/S0167-6393(98)00019-3_BIB8 doi: 10.1109/T-C.1974.223784 – ident: 10.1016/S0167-6393(98)00019-3_BIB3 doi: 10.1109/TASSP.1984.1164453 – ident: 10.1016/S0167-6393(98)00019-3_BIB6 doi: 10.1109/ICASSP.1979.1170788 – ident: 10.1016/S0167-6393(98)00019-3_BIB4 doi: 10.1109/TASSP.1979.1163209 – ident: 10.1016/S0167-6393(98)00019-3_BIB15 doi: 10.1109/ICASSP.1996.543199 – volume: 8 start-page: 387 year: 1985 ident: 10.1016/S0167-6393(98)00019-3_BIB12 article-title: Noise suppression by spectral magnitude estimation – Mechanism and theoretical limits publication-title: Signal Processing doi: 10.1016/0165-1684(85)90002-7 contributor: fullname: Vary – ident: 10.1016/S0167-6393(98)00019-3_BIB9 doi: 10.1109/TASSP.1977.1162974 – ident: 10.1016/S0167-6393(98)00019-3_BIB11 doi: 10.1109/TASSP.1980.1163353 – ident: 10.1016/S0167-6393(98)00019-3_BIB1 doi: 10.1109/TASSP.1976.1162870 – ident: 10.1016/S0167-6393(98)00019-3_BIB16 doi: 10.1109/89.279283 – ident: 10.1016/S0167-6393(98)00019-3_BIB5 doi: 10.1109/TASSP.1980.1163394 – ident: 10.1016/S0167-6393(98)00019-3_BIB2 doi: 10.21437/ICSLP.1990-295 – ident: 10.1016/S0167-6393(98)00019-3_BIB13 doi: 10.1109/TCOM.1977.1093941
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Snippet	This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose... The advantage of using the discrete cosine transform (DCT) over the standard discrete Fourier transform (DFT) for the purpose of removing noise embedded in a...
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SubjectTerms	Applied sciences Discrete cosine transform (DCT) Exact sciences and technology Information, signal and communications theory MMSE amplitude estimation Noise removal Signal processing Speech enhancement Speech processing Telecommunications and information theory
Title	Noisy speech enhancement using discrete cosine transform
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