The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Biing-hwang Juang - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A family of distortion measures base upon Projection Operation for robust speech recognition
ICASSP-88. International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: D. Mansour, Biing-hwang JuangAbstract:The authors aim at the formulation of similarity measures for robust speech recognition. Their consideration focuses on the speech cepstrum derived from linear prediction coefficients (the LPC cepstrum). By using common models for noisy speech, they analytically and empirically show how the ambient noise can affect some important attributes of the LPC cepstrum such as the vector norm, coefficient order, and the direction perturbation. The new findings led them to propose a family of distortion measures based on the Projection between two cepstral vectors. Performance evaluation of these measures has been conducted in both speaker-dependent and speaker-independent isolated word recognition tasks. Experimental results show that the new measures cause no degradation in recognition accuracy at high SNR, but perform significantly better when tested under noisy conditions using only clean reference templates. At an SNR of 5 dB, the new measures are shown to be able to achieve a recognition rate equivalent to that obtained by the filtered cepstral measure at 20 dB SNR, demonstrating a gain of 15 dB. >
Linda G Demichiel - One of the best experts on this subject based on the ideXlab platform.
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type derivation using the Projection Operation
Extending Database Technology, 1994Co-Authors: Rakesh Agrawal, Linda G DemichielAbstract:We present techniques for deriving types from existing objectoriented types using the relational algebraic Projection Operation and for inferring the methods that are applicable to these types. Such type derivation occurs, for example, as a result of defining algebraic views over object types. We refactor the type hierarchy and place the derived types in the type hierarchy in such a way that the state and behavior of existing types remain exactly as before. Our results have applicability to relational databases extended with object-oriented type systems and to object-oriented systems that support algebraic Operations.
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EDBT - Type derivation using the Projection Operation
Information Systems, 1994Co-Authors: Rakesh Agrawal, Linda G DemichielAbstract:We present techniques for deriving types from existing objectoriented types using the relational algebraic Projection Operation and for inferring the methods that are applicable to these types. Such type derivation occurs, for example, as a result of defining algebraic views over object types. We refactor the type hierarchy and place the derived types in the type hierarchy in such a way that the state and behavior of existing types remain exactly as before. Our results have applicability to relational databases extended with object-oriented type systems and to object-oriented systems that support algebraic Operations.
D. Mansour - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A family of distortion measures base upon Projection Operation for robust speech recognition
ICASSP-88. International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: D. Mansour, Biing-hwang JuangAbstract:The authors aim at the formulation of similarity measures for robust speech recognition. Their consideration focuses on the speech cepstrum derived from linear prediction coefficients (the LPC cepstrum). By using common models for noisy speech, they analytically and empirically show how the ambient noise can affect some important attributes of the LPC cepstrum such as the vector norm, coefficient order, and the direction perturbation. The new findings led them to propose a family of distortion measures based on the Projection between two cepstral vectors. Performance evaluation of these measures has been conducted in both speaker-dependent and speaker-independent isolated word recognition tasks. Experimental results show that the new measures cause no degradation in recognition accuracy at high SNR, but perform significantly better when tested under noisy conditions using only clean reference templates. At an SNR of 5 dB, the new measures are shown to be able to achieve a recognition rate equivalent to that obtained by the filtered cepstral measure at 20 dB SNR, demonstrating a gain of 15 dB. >
Rakesh Agrawal - One of the best experts on this subject based on the ideXlab platform.
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type derivation using the Projection Operation
Extending Database Technology, 1994Co-Authors: Rakesh Agrawal, Linda G DemichielAbstract:We present techniques for deriving types from existing objectoriented types using the relational algebraic Projection Operation and for inferring the methods that are applicable to these types. Such type derivation occurs, for example, as a result of defining algebraic views over object types. We refactor the type hierarchy and place the derived types in the type hierarchy in such a way that the state and behavior of existing types remain exactly as before. Our results have applicability to relational databases extended with object-oriented type systems and to object-oriented systems that support algebraic Operations.
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EDBT - Type derivation using the Projection Operation
Information Systems, 1994Co-Authors: Rakesh Agrawal, Linda G DemichielAbstract:We present techniques for deriving types from existing objectoriented types using the relational algebraic Projection Operation and for inferring the methods that are applicable to these types. Such type derivation occurs, for example, as a result of defining algebraic views over object types. We refactor the type hierarchy and place the derived types in the type hierarchy in such a way that the state and behavior of existing types remain exactly as before. Our results have applicability to relational databases extended with object-oriented type systems and to object-oriented systems that support algebraic Operations.
Angelia Nedic - One of the best experts on this subject based on the ideXlab platform.
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distributed random Projection algorithm for convex optimization
IEEE Journal of Selected Topics in Signal Processing, 2013Co-Authors: Soomin Lee, Angelia NedicAbstract:Random Projection algorithm is of interest for constrained optimization when the constraint set is not known in advance or the Projection Operation on the whole constraint set is computationally prohibitive. This paper presents a distributed random Projection algorithm for constrained convex optimization problems that can be used by multiple agents connected over a time-varying network, where each agent has its own objective function and its own constrained set. We prove that the iterates of all agents converge to the same point in the optimal set almost surely. Experiments on distributed support vector machines demonstrate good performance of the algorithm.
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random Projection algorithms for convex set intersection problems
Conference on Decision and Control, 2010Co-Authors: Angelia NedicAbstract:The focus of this paper is on the set intersection problem for closed convex sets admitting Projection Operation in a closed form. The objective is to investigate algorithms that would converge (in some sense) if and only if the problem has a solution. To do so, we view the set intersection problem as a stochastic optimization problem of minimizing the “average” residual error of the set collection. We consider a stochastic gradient method as a main tool for investigating the properties of the stochastic optimization problem. We show that the stochastic optimization problem has a solution if and only if the stochastic gradient method is convergent almost surely. We then consider a special case of the method, namely the random Projection method, and we analyze its convergence. We show that a solution of the intersection problem exists if and only if the random Projection method exhibits certain convergence behavior almost surely. In addition, we provide convergence rate results for the expected residual error.
