The Experts below are selected from a list of 21417 Experts worldwide ranked by ideXlab platform
Ismo Karkkainen - One of the best experts on this subject based on the ideXlab platform.
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SSPR/SPR - Class-Discriminative Weighted Distortion Measure for VQ-based Speaker Identification
Lecture Notes in Computer Science, 2002Co-Authors: Tomi Kinnunen, Ismo KarkkainenAbstract:We consider the Distortion Measure in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the Distortions between the unknown speech sample and the models in the speaker database. In this paper, we introduce a weighted Distortion Measure that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa.
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class discriminative weighted Distortion Measure for vq based speaker identification
Lecture Notes in Computer Science, 2002Co-Authors: Tomi Kinnunen, Ismo KarkkainenAbstract:We consider the Distortion Measure in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the Distortions between the unknown speech sample and the models in the speaker database. In this paper, we introduce a weighted Distortion Measure that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa.
Thomas Eriksson - One of the best experts on this subject based on the ideXlab platform.
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a speech spectrum Distortion Measure with interframe memory
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: Fredrik Nordin, Thomas ErikssonAbstract:We present a novel spectral Distortion Measure with interframe memory. The memory gives the possibility to take into account the dynamics of the time evolution of the speech spectrum, which has shown to have a significant importance on the perceived speech quality. Memory is introduced by linear filtering of the time evolution of the difference log spectrum. This facilitates smoothing of spectrum with an ability to track quick transitions. Our results point at a substantially improved performance when rapidly evolving spectrum errors are punished in the Measure.
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ICASSP - A speech spectrum Distortion Measure with interframe memory
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Fredrik Nordin, Thomas ErikssonAbstract:We present a novel spectral Distortion Measure with interframe memory. The memory gives the possibility to take into account the dynamics of the time evolution of the speech spectrum, which has shown to have a significant importance on the perceived speech quality. Memory is introduced by linear filtering of the time evolution of the difference log spectrum. This facilitates smoothing of spectrum with an ability to track quick transitions. Our results point at a substantially improved performance when rapidly evolving spectrum errors are punished in the Measure.
Richard C Hendriks - One of the best experts on this subject based on the ideXlab platform.
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speech reinforcement with a globally optimized perceptual Distortion Measure for noisy reverberant channels
International Workshop on Acoustic Signal Enhancement, 2014Co-Authors: Joao B Crespo, Richard C HendriksAbstract:In this paper, a time-frequency weighting is proposed for speech reinforcement (near-end listening enhancement) in a noisy and reverberant environment, which optimizes a perceptual Distortion Measure globally for a number of time-frequency bins. Simulations confirm the optimality of the algorithm and a comparison is made to three reference methods using two additional instrumental Measures.
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speech reinforcement in noisy reverberant environments using a perceptual Distortion Measure
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Joao B Crespo, Richard C HendriksAbstract:In this paper, a time-frequency weighting is proposed for speech reinforcement (near-end listening enhancement) in a noisy and reverberant environment, which optimizes a perceptual Distortion Measure locally for each time-frequency bin. The algorithm acts as a dynamic range compressor, smearing out the energy of the clean speech along time. Simulations predict an intelligibility increase with respect to the unprocessed condition and two reference methods, for moderate smoothing windows, as Measured by the optimized Distortion Measure and two objective intelligibility Measures.
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IWAENC - Speech reinforcement with a globally optimized perceptual Distortion Measure for noisy reverberant channels
2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC), 2014Co-Authors: Joao B Crespo, Richard C HendriksAbstract:In this paper, a time-frequency weighting is proposed for speech reinforcement (near-end listening enhancement) in a noisy and reverberant environment, which optimizes a perceptual Distortion Measure globally for a number of time-frequency bins. Simulations confirm the optimality of the algorithm and a comparison is made to three reference methods using two additional instrumental Measures.
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ICASSP - Speech reinforcement in noisy reverberant environments using a perceptual Distortion Measure
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Joao B Crespo, Richard C HendriksAbstract:In this paper, a time-frequency weighting is proposed for speech reinforcement (near-end listening enhancement) in a noisy and reverberant environment, which optimizes a perceptual Distortion Measure locally for each time-frequency bin. The algorithm acts as a dynamic range compressor, smearing out the energy of the clean speech along time. Simulations predict an intelligibility increase with respect to the unprocessed condition and two reference methods, for moderate smoothing windows, as Measured by the optimized Distortion Measure and two objective intelligibility Measures.
Tomi Kinnunen - One of the best experts on this subject based on the ideXlab platform.
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SSPR/SPR - Class-Discriminative Weighted Distortion Measure for VQ-based Speaker Identification
Lecture Notes in Computer Science, 2002Co-Authors: Tomi Kinnunen, Ismo KarkkainenAbstract:We consider the Distortion Measure in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the Distortions between the unknown speech sample and the models in the speaker database. In this paper, we introduce a weighted Distortion Measure that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa.
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class discriminative weighted Distortion Measure for vq based speaker identification
Lecture Notes in Computer Science, 2002Co-Authors: Tomi Kinnunen, Ismo KarkkainenAbstract:We consider the Distortion Measure in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the Distortions between the unknown speech sample and the models in the speaker database. In this paper, we introduce a weighted Distortion Measure that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa.
V.j. Mathews - One of the best experts on this subject based on the ideXlab platform.
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Vector quantization using the L/sub /spl infin// Distortion Measure
IEEE Signal Processing Letters, 1997Co-Authors: V.j. Mathews, P.j. HahnAbstract:This paper considers vector quantization of signals using the L/sub /spl infin// Distortion Measure. The key contribution is a result that allows one to characterize the centroid of a set of vectors for the L/sub /spl infin// Distortion Measure. A method similar to the Linde-Buzo-Gray (LBG) algorithm for designing codebooks has been developed and tested. The paper also discusses the design of vector quantizers employing the L/sub /spl infin// Distortion Measure in an application in which the occurrences of quantization errors with larger magnitudes than a preselected threshold must be minimized.
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Multiplication free vector quantization using L/sub 1/ Distortion Measure and its variants
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1992Co-Authors: V.j. MathewsAbstract:The author considers vector quantization that uses the L/sub 1/ Distortion Measure for its implementation. A gradient-based approach for codebook design that does not require any multiplications or median computation is proposed. Convergence of this method is proved rigorously under very mild conditions. Simulation examples comparing the performance of this technique with the LBG algorithm show that the gradient-based method, in spite of its simplicity, produces codebooks with average Distortions that are comparable to the LBG algorithm. The codebook design algorithm is then extended to a Distortion Measure that has piecewise-linear characteristics. Once again, by appropriate selection of the parameters of the Distortion Measure, the encoding as well as the codebook design can be implemented with zero multiplications. The author applies the techniques in predictive vector quantization of images and demonstrates the viability of multiplication-free predictive vector quantization of image data. >
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ICASSP - Multiplication-free vector quantization using L/sub 1/ Distortion Measure and its variants
International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: V.j. Mathews, M. KhorchidianAbstract:The authors first consider vector quantization that uses the L/sub 1/ Distortion Measure for its implementation. The L/sub 1/ Distortion Measure is very attractive from an implementational point of view, since no multiplication is required for computing the Distortion Measure. Unfortunately, the traditional Linde-Buzo-Gray method (1980) for designing the codebook for the L/sub 1/ Distortion Measure can become extremely time-consuming, since it involves several computations of medians of very large arrays. The authors propose a gradient-based approach for codebook design that does not require any multiplications or median computations. The codebook design algorithm is then extended to a Distortion Measure that has piecewise-linear characteristics. By appropriate selection of the parameters of the Distortion Measure, the encoding as well as the codebook design can be implemented with zero multiplications. The authors apply the proposed techniques in predictive vector quantization of images and demonstrate the viability of multiplication-free predictive vector quantization of image data. >
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ICIP - Vector quantization of images using the L/sub /spl infin// Distortion Measure
Proceedings. International Conference on Image Processing, 1Co-Authors: V.j. MathewsAbstract:This paper considers vector quantization of signals using the L/sub /spl infin// Distortion Measure. The key contribution is a result that allows one to characterize the centroid of a set of vectors for the L/sub /spl infin// Distortion Measure. A method similar to the LEG algorithm for designing codebooks has been developed and tested. The paper also discusses the design of vector quantizers employing the L/sub /spl infin// Distortion Measure in an application in which the occurrences of quantization errors with larger magnitudes than a pre-selected threshold must be minimized.