The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
T V Sreenivas - One of the best experts on this subject based on the ideXlab platform.
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Switched Conditional Pdf-Based Split VQ Using Gaussian Mixture Model
2015Co-Authors: Saikat Chatterjee, T V Sreenivas, Student Member, Senior MemberAbstract:Abstract—In this letter, we develop switched Conditional Pdf-based split vector quantization (SCSVQ) method using the recently proposed Conditional Pdf-based split vector quantizer (CSVQ). The use of CSVQ allows us to alleviate the coding loss by exploiting the correlation between subvectors, in each switching region. Using the Gaussian mixture model (GMM)-based para-metric framework, we also address the rate-distortion (R/D) performance optimality of the proposed SCSVQ method by allo-cating the bits optimally among the switching regions. For the wideband speech line spectrum frequency (LSF) pa-rameter quantization, it is shown that the optimum parametric SCSVQ method provides nearly 2 bits/vector advantage over the recently proposed nonparametric switched split vector quantiza-tion (SSVQ) method. Index Terms—Gaussian mixture model (GMM), line spectrum frequency (LSF) coding, vector quantization. I
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Switched Conditional Pdf-Based Split VQ Using Gaussian Mixture Model
IEEE, 2008Co-Authors: Chatterjee Saikat, T V SreenivasAbstract:In this letter, we develop switched Conditional Pdf-based split vector quantization (SCSVQ) method using the recently proposed Conditional Pdf-based split vector quantizer (CSVQ). The use of CSVQ allows us to alleviate the coding loss by exploiting the correlation between subvectors, in each switching region. Using the Gaussian mixture model (GMM)-based parametric framework, we also address the rate-distortion (R/D) performance optimality of the proposed SCSVQ method by allocating the bits optimally among the switching regions. For the wideband speech line spectrum frequency (LSF) parameter quantization, it is shown that the optimum parametric SCSVQ method provides nearly 2 bits/vector advantage over the recently proposed nonparametric switched split vector quantization (SSVQ) method
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analysis of Conditional Pdf based split vq
IEEE Signal Processing Letters, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:The split vector quantization (SVQ) method results in a ldquocoding lossrdquo due to the independent quantization of split subvectors. To recover the coding loss, Conditional Pdf-based split vector quantization methods were proposed recently. For a multivariate Gaussian source, we theoretically derive the expression of rate-distortion (R/D) performance for Conditional Pdf-based split vector quantization (CSVQ) method, using high rate quantization theory and optimum bit allocation. We also derive the rate-distortion performance expression of traditional SVQ method and, thus, quantify the coding gain of CSVQ method over SVQ method.
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Conditional Pdf based split vector quantization of wideband lsf parameters
IEEE Signal Processing Letters, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:The commonly used split vector quantization (SVQ) method is inferior to unconstrained quantization due to independent coding of the split subvectors, resulting in a coding loss. In this paper, we propose a Conditional Pdf-based split vector quantization (CSVQ) method to recover the coding loss. The CSVQ method is developed assuming the line spectrum frequency source distribution as a multivariate Gaussian and the subvectors are quantized sequentially to exploit the correlation between the subvectors. The new CSVQ method is evaluated for wideband speech LSF quantization; CSVQ is shown to outperform traditional SVQ and provide comparable performance to the recently proposed switched split vector quantization (SSVQ) method. In addition, the transform domain SVQ method is also realized to show that its performance is limited by the distance measure used in the transform domain.
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sequential split vector quantization of lsf parameters using Conditional Pdf
International Conference on Acoustics Speech and Signal Processing, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses Conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.
Saikat Chatterjee - One of the best experts on this subject based on the ideXlab platform.
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Switched Conditional Pdf-Based Split VQ Using Gaussian Mixture Model
2015Co-Authors: Saikat Chatterjee, T V Sreenivas, Student Member, Senior MemberAbstract:Abstract—In this letter, we develop switched Conditional Pdf-based split vector quantization (SCSVQ) method using the recently proposed Conditional Pdf-based split vector quantizer (CSVQ). The use of CSVQ allows us to alleviate the coding loss by exploiting the correlation between subvectors, in each switching region. Using the Gaussian mixture model (GMM)-based para-metric framework, we also address the rate-distortion (R/D) performance optimality of the proposed SCSVQ method by allo-cating the bits optimally among the switching regions. For the wideband speech line spectrum frequency (LSF) pa-rameter quantization, it is shown that the optimum parametric SCSVQ method provides nearly 2 bits/vector advantage over the recently proposed nonparametric switched split vector quantiza-tion (SSVQ) method. Index Terms—Gaussian mixture model (GMM), line spectrum frequency (LSF) coding, vector quantization. I
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analysis of Conditional Pdf based split vq
IEEE Signal Processing Letters, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:The split vector quantization (SVQ) method results in a ldquocoding lossrdquo due to the independent quantization of split subvectors. To recover the coding loss, Conditional Pdf-based split vector quantization methods were proposed recently. For a multivariate Gaussian source, we theoretically derive the expression of rate-distortion (R/D) performance for Conditional Pdf-based split vector quantization (CSVQ) method, using high rate quantization theory and optimum bit allocation. We also derive the rate-distortion performance expression of traditional SVQ method and, thus, quantify the coding gain of CSVQ method over SVQ method.
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Conditional Pdf based split vector quantization of wideband lsf parameters
IEEE Signal Processing Letters, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:The commonly used split vector quantization (SVQ) method is inferior to unconstrained quantization due to independent coding of the split subvectors, resulting in a coding loss. In this paper, we propose a Conditional Pdf-based split vector quantization (CSVQ) method to recover the coding loss. The CSVQ method is developed assuming the line spectrum frequency source distribution as a multivariate Gaussian and the subvectors are quantized sequentially to exploit the correlation between the subvectors. The new CSVQ method is evaluated for wideband speech LSF quantization; CSVQ is shown to outperform traditional SVQ and provide comparable performance to the recently proposed switched split vector quantization (SSVQ) method. In addition, the transform domain SVQ method is also realized to show that its performance is limited by the distance measure used in the transform domain.
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sequential split vector quantization of lsf parameters using Conditional Pdf
International Conference on Acoustics Speech and Signal Processing, 2007Co-Authors: Saikat Chatterjee, T V SreenivasAbstract:A better performing product code vector quantization (VQ) method is proposed for coding the line spectrum frequency (LSF) parameters; the method is referred to as sequential split vector quantization (SeSVQ). The split sub-vectors of the full LSF vector are quantized in sequence and thus uses Conditional distribution derived from the previous quantized sub-vectors. Unlike the traditional split vector quantization (SVQ) method, SeSVQ exploits the inter sub-vector correlation and thus provides improved rate-distortion performance, but at the expense of higher memory. We investigate the quantization performance of SeSVQ over traditional SVQ and transform domain split VQ (TrSVQ) methods. Compared to SVQ, SeSVQ saves 1 bit and nearly 3 bits, for telephone-band and wide-band speech coding applications respectively.
Chatterjee Saikat - One of the best experts on this subject based on the ideXlab platform.
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Switched Conditional Pdf-Based Split VQ Using Gaussian Mixture Model
IEEE, 2008Co-Authors: Chatterjee Saikat, T V SreenivasAbstract:In this letter, we develop switched Conditional Pdf-based split vector quantization (SCSVQ) method using the recently proposed Conditional Pdf-based split vector quantizer (CSVQ). The use of CSVQ allows us to alleviate the coding loss by exploiting the correlation between subvectors, in each switching region. Using the Gaussian mixture model (GMM)-based parametric framework, we also address the rate-distortion (R/D) performance optimality of the proposed SCSVQ method by allocating the bits optimally among the switching regions. For the wideband speech line spectrum frequency (LSF) parameter quantization, it is shown that the optimum parametric SCSVQ method provides nearly 2 bits/vector advantage over the recently proposed nonparametric switched split vector quantization (SSVQ) method
Milos Doroslovacki - One of the best experts on this subject based on the ideXlab platform.
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proportionate type nlms algorithms based on maximization of the joint Conditional Pdf for the weight deviation vector
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Kevin T Wagner, Milos DoroslovackiAbstract:In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint Conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the joint maximum Conditional probability density function (Pdf) one-step algorithm is superior to the proportionate normalized least mean square algorithm when operating on a sparse impulse response. We also show that the new algorithm is superior to a previously introduced algorithm which assumed that the Conditional Pdf could be represented by the product of the marginal Conditional Pdfs, i.e., that the weight deviations are mutually Conditionally independent.
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proportional type nlms algorithm with gain allocation providing maximum one step Conditional Pdf for true weights
Conference on Information Sciences and Systems, 2009Co-Authors: Kevin T Wagner, Milos DoroslovackiAbstract:In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the Conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the maximum Conditional probability density one-step algorithm is superior to the normalized least mean square algorithm and the proportionate normalized least mean square algorithm. Additionally, we argue that the algorithm we present operates for any impulse response.
Kevin T Wagner - One of the best experts on this subject based on the ideXlab platform.
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proportionate type nlms algorithms based on maximization of the joint Conditional Pdf for the weight deviation vector
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Kevin T Wagner, Milos DoroslovackiAbstract:In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the joint Conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the joint maximum Conditional probability density function (Pdf) one-step algorithm is superior to the proportionate normalized least mean square algorithm when operating on a sparse impulse response. We also show that the new algorithm is superior to a previously introduced algorithm which assumed that the Conditional Pdf could be represented by the product of the marginal Conditional Pdfs, i.e., that the weight deviations are mutually Conditionally independent.
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proportional type nlms algorithm with gain allocation providing maximum one step Conditional Pdf for true weights
Conference on Information Sciences and Systems, 2009Co-Authors: Kevin T Wagner, Milos DoroslovackiAbstract:In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the Conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the maximum Conditional probability density one-step algorithm is superior to the normalized least mean square algorithm and the proportionate normalized least mean square algorithm. Additionally, we argue that the algorithm we present operates for any impulse response.