A New Approach for Blind Source Separation of Convolutive Sources - Wavelet Based Separation Using Shrinkage Function

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9783639077971: A New Approach for Blind Source Separation of Convolutive Sources - Wavelet Based Separation Using Shrinkage Function
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ICA and its variations are used extensively in BSS. Most of the algorithms that are used to separate speech or music signals utilize ICA in the time frequency domain. Here ICA is applied in the wavelet domain. Separation of signals is achieved by applying the ICA algorithm and shrinkage functions to the wavelet coefficients of the original mixtures. ICA alone can achieve reasonably good separation of artificially convolved sources; however, poor separation quality is experienced for real world convolutive mixtures. This work presents a novel post processing technique to deal with the cross talk problem. The post processor is applied to the signals separated by the ICA network. A super Gaussian form of the PDF is assumed for the dominant source components. Closed form solutions of the parameters of the PDF are obtained by the MOM. The PDF of the cross talk components is assumed to be of a GMM, and the EM method is applied to determine the parameters of the Gaussian mixtures. The algorithm is applied to a real world mixture of music and speech signals. The results show a significant reduction in the cross talk.

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Book Description Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | Wavelet Based Separation Using Shrinkage Function | ICA and its variations are used extensively in BSS.Most of the algorithms that are used to separatespeech or music signals utilize ICA in the timefrequency domain. Here ICA is applied in the waveletdomain. Separation of signals is achieved byapplying the ICA algorithm and shrinkage functionsto the wavelet coefficients of the originalmixtures. ICA alone can achieve reasonably goodseparation of artificially convolved sources;however, poor separation quality is experienced forreal world convolutive mixtures. This work presentsa novel post processing technique to deal with thecross talk problem. The post processor is applied tothe signals separated by the ICA network. A superGaussian form of the PDF is assumed for the dominantsource components. Closed form solutions of theparameters of the PDF are obtained by the MOM. ThePDF of the cross talk components is assumed to be ofa GMM, and the EM method is applied to determine theparameters of the Gaussian mixtures. The algorithmis applied to a real world mixture of music andspeech signals. The results show a significantreduction in the cross talk. | Format: Paperback | Language/Sprache: english | 128 gr | 220x150x4 mm | 84 pp. Seller Inventory # K9783639077971

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Book Description VDM Verlag Dr. Mueller e.K., Germany, 2013. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.ICA and its variations are used extensively in BSS. Most of the algorithms that are used to separate speech or music signals utilize ICA in the time frequency domain. Here ICA is applied in the wavelet domain. Separation of signals is achieved by applying the ICA algorithm and shrinkage functions to the wavelet coefficients of the original mixtures. ICA alone can achieve reasonably good separation of artificially convolved sources; however, poor separation quality is experienced for real world convolutive mixtures. This work presents a novel post processing technique to deal with the cross talk problem. The post processor is applied to the signals separated by the ICA network. A super Gaussian form of the PDF is assumed for the dominant source components. Closed form solutions of the parameters of the PDF are obtained by the MOM. The PDF of the cross talk components is assumed to be of a GMM, and the EM method is applied to determine the parameters of the Gaussian mixtures. The algorithm is applied to a real world mixture of music and speech signals. The results show a significant reduction in the cross talk. Seller Inventory # AAV9783639077971

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Book Description VDM Verlag Dr. Müller E.K. Okt 2013, 2013. Taschenbuch. Condition: Neu. Neuware - ICA and its variations are used extensively in BSS.Most of the algorithms that are used to separatespeech or music signals utilize ICA in the timefrequency domain. Here ICA is applied in the waveletdomain. Separation of signals is achieved byapplying the ICA algorithm and shrinkage functionsto the wavelet coefficients of the originalmixtures. ICA alone can achieve reasonably goodseparation of artificially convolved sources;however, poor separation quality is experienced forreal world convolutive mixtures. This work presentsa novel post processing technique to deal with thecross talk problem. The post processor is applied tothe signals separated by the ICA network. A superGaussian form of the PDF is assumed for the dominantsource components. Closed form solutions of theparameters of the PDF are obtained by the MOM. ThePDF of the cross talk components is assumed to be ofa GMM, and the EM method is applied to determine theparameters of the Gaussian mixtures. The algorithmis applied to a real world mixture of music andspeech signals. The results show a significantreduction in the cross talk. 84 pp. Englisch. Seller Inventory # 9783639077971

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Book Description VDM Verlag Dr. Müller E.K. Okt 2013, 2013. Taschenbuch. Condition: Neu. Neuware - ICA and its variations are used extensively in BSS.Most of the algorithms that are used to separatespeech or music signals utilize ICA in the timefrequency domain. Here ICA is applied in the waveletdomain. Separation of signals is achieved byapplying the ICA algorithm and shrinkage functionsto the wavelet coefficients of the originalmixtures. ICA alone can achieve reasonably goodseparation of artificially convolved sources;however, poor separation quality is experienced forreal world convolutive mixtures. This work presentsa novel post processing technique to deal with thecross talk problem. The post processor is applied tothe signals separated by the ICA network. A superGaussian form of the PDF is assumed for the dominantsource components. Closed form solutions of theparameters of the PDF are obtained by the MOM. ThePDF of the cross talk components is assumed to be ofa GMM, and the EM method is applied to determine theparameters of the Gaussian mixtures. The algorithmis applied to a real world mixture of music andspeech signals. The results show a significantreduction in the cross talk. 84 pp. Englisch. Seller Inventory # 9783639077971

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Book Description VDM Verlag Dr. Mueller e.K. Paperback. Condition: New. 84 pages. Dimensions: 8.8in. x 5.9in. x 0.4in.ICA and its variations are used extensively in BSS. Most of the algorithms that are used to separate speech or music signals utilize ICA in the time frequency domain. Here ICA is applied in the wavelet domain. Separation of signals is achieved by applying the ICA algorithm and shrinkage functions to the wavelet coefficients of the original mixtures. ICA alone can achieve reasonably good separation of artificially convolved sources; however, poor separation quality is experienced for real world convolutive mixtures. This work presents a novel post processing technique to deal with the cross talk problem. The post processor is applied to the signals separated by the ICA network. A super Gaussian form of the PDF is assumed for the dominant source components. Closed form solutions of the parameters of the PDF are obtained by the MOM. The PDF of the cross talk components is assumed to be of a GMM, and the EM method is applied to determine the parameters of the Gaussian mixtures. The algorithm is applied to a real world mixture of music and speech signals. The results show a significant reduction in the cross talk. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783639077971

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Book Description VDM Verlag Dr. Mueller e.K., 2008. Paperback. Condition: New. Seller Inventory # DADAX3639077970

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