Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model
Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is model...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lotter, Thomas [verfasserIn] Vary, Peter [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Übergeordnetes Werk: |
Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2005(2005), 7 vom: 22. Mai |
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| Übergeordnetes Werk: |
volume:2005 ; year:2005 ; number:7 ; day:22 ; month:05 |
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| DOI / URN: |
10.1155/ASP.2005.1110 |
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| 520 | |a Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. | ||
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10.1155/ASP.2005.1110 doi (DE-627)SPR031980333 (SPR)ASP.2005.1110-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lotter, Thomas verfasserin aut Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. Vary, Peter verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2005(2005), 7 vom: 22. Mai (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2005 year:2005 number:7 day:22 month:05 https://dx.doi.org/10.1155/ASP.2005.1110 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2005 2005 7 22 05 |
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10.1155/ASP.2005.1110 doi (DE-627)SPR031980333 (SPR)ASP.2005.1110-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lotter, Thomas verfasserin aut Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. Vary, Peter verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2005(2005), 7 vom: 22. Mai (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2005 year:2005 number:7 day:22 month:05 https://dx.doi.org/10.1155/ASP.2005.1110 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2005 2005 7 22 05 |
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10.1155/ASP.2005.1110 doi (DE-627)SPR031980333 (SPR)ASP.2005.1110-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lotter, Thomas verfasserin aut Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. Vary, Peter verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2005(2005), 7 vom: 22. Mai (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2005 year:2005 number:7 day:22 month:05 https://dx.doi.org/10.1155/ASP.2005.1110 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2005 2005 7 22 05 |
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10.1155/ASP.2005.1110 doi (DE-627)SPR031980333 (SPR)ASP.2005.1110-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lotter, Thomas verfasserin aut Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. Vary, Peter verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2005(2005), 7 vom: 22. Mai (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2005 year:2005 number:7 day:22 month:05 https://dx.doi.org/10.1155/ASP.2005.1110 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2005 2005 7 22 05 |
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10.1155/ASP.2005.1110 doi (DE-627)SPR031980333 (SPR)ASP.2005.1110-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Lotter, Thomas verfasserin aut Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. Vary, Peter verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2005(2005), 7 vom: 22. Mai (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2005 year:2005 number:7 day:22 month:05 https://dx.doi.org/10.1155/ASP.2005.1110 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_161 GBV_ILN_170 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2522 53.73 ASE AR 2005 2005 7 22 05 |
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Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model |
| abstract |
Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. |
| abstractGer |
Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. |
| abstract_unstemmed |
Abstract This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm. |
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Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model |
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