The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
Atsushi Shimizu - One of the best experts on this subject based on the ideXlab platform.
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complexity reduction algorithm for Optimum Quantizer design based on amplitude sparseness
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
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ICASSP - Complexity Reduction Algorithm for Optimum Quantizer Design Based on Amplitude Sparseness
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
Yukihiro Bandoh - One of the best experts on this subject based on the ideXlab platform.
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complexity reduction algorithm for Optimum Quantizer design based on amplitude sparseness
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
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ICASSP - Complexity Reduction Algorithm for Optimum Quantizer Design Based on Amplitude Sparseness
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
Seishi Takamura - One of the best experts on this subject based on the ideXlab platform.
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complexity reduction algorithm for Optimum Quantizer design based on amplitude sparseness
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
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ICASSP - Complexity Reduction Algorithm for Optimum Quantizer Design Based on Amplitude Sparseness
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Yukihiro Bandoh, Seishi Takamura, Atsushi ShimizuAbstract:The design of an Optimum Quantizer can be formulated as an optimization problem that finds the quantization indices that minimize the quantization error. One solution of the optimization problem is DP quantization, an approach based on dynamic programming. It is known that a quantized signal does not always contain signal values that can be represented with a given bit-depth. This property is called amplitude sparseness. Because quantization is the amplitude discretization of signal value, amplitude sparseness is closely related to the design of the Quantizer. Since signal values with zero frequency do not affect quantization error, there is the potential to reduce complexity when designing the Optimum Quantizer by skipping the processing of signal values that have zero frequency. However, conventional methods on DP quantization do not design for amplitude sparseness and so are unduly complex. In this paper, we propose an algorithm that yields an Optimum Quantizer that minimizes quantization error with reduced complexity given the existence of amplitude sparseness.
Taek Sang Oh - One of the best experts on this subject based on the ideXlab platform.
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approximation of Optimum Quantizer detector using a uniform Quantizer and a coder
Signal Processing, 1991Co-Authors: Iickho Song, Taek Sang OhAbstract:Abstract A subOptimum quantization-detection system in which the locally Optimum nonlinearity is replaced by a uniform Quantizer and a coder for the uniform Quantizer output levels is proposed. The proposed detection system does not require iterations to obtain the parameters of the Quantizer and is easily implementable in practice. We develop a design procedure of the subOptimum Quantizer and obtain the parameters of the subOptimum Quantizer for the generalized Gaussian noise density. We also compare the performance of the subOptimum quantization detector with that of other detectors.
Thomas P. Barnwell - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - An algorithm for designing Optimum Quantizers subject to a multiclass distortion criterion
ICASSP '83. IEEE International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: J. Crosmer, Thomas P. BarnwellAbstract:This paper describes an extension of the Lloyd-Max Optimum Quantizer design algorithm to a multi-class mean-square-error optimally criterion. As part of this study, a comparison of uniform versus non-uniform gathering of bins in histograms used to approximate probability density studies is presented. In the associated experimental study, the new Quantizer design procedure is applied to a forward adaptive ADPCM coder, and the resulting convergence, speed of computation, and final error statistics are examined. Listening tests indicate that the multi-class design procedure produces a clearly perceivable, though modest, improvement in the speech quality.