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Meir Feder - One of the best experts on this subject based on the ideXlab platform.
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universal communication over modulo additive individual Noise Sequence channels
International Symposium on Information Theory, 2011Co-Authors: Yuval Lomnitz, Meir FederAbstract:Which communication rates can be attained over a channel whose output is an unknown (possibly stochastic) function of the input that may vary arbitrarily in time with no a-priori model? Following the spirit of the finite-state compressibility of a Sequence defined by Lempel and Ziv, we define a “capacity” for such a channel as the highest rate achievable by a designer knowing the particular relation that indeed exists between the input and output for all times, yet is constrained to use a fixed finite-length block communication scheme (i.e., use the same scheme over each block). In the case of the binary modulo additive channel, where the output Sequence is obtained by modulo addition of an unknown individual Sequence to the input Sequence, this capacity is upper bounded by 1 − ρ where ρ is the finite state compressibility of the Noise Sequence. We present a communication scheme with feedback that attains this rate universally without prior knowledge of the Noise Sequence.
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ISIT - Universal communication over modulo-additive individual Noise Sequence channels
2011 IEEE International Symposium on Information Theory Proceedings, 2011Co-Authors: Yuval Lomnitz, Meir FederAbstract:Which communication rates can be attained over a channel whose output is an unknown (possibly stochastic) function of the input that may vary arbitrarily in time with no a-priori model? Following the spirit of the finite-state compressibility of a Sequence defined by Lempel and Ziv, we define a “capacity” for such a channel as the highest rate achievable by a designer knowing the particular relation that indeed exists between the input and output for all times, yet is constrained to use a fixed finite-length block communication scheme (i.e., use the same scheme over each block). In the case of the binary modulo additive channel, where the output Sequence is obtained by modulo addition of an unknown individual Sequence to the input Sequence, this capacity is upper bounded by 1 − ρ where ρ is the finite state compressibility of the Noise Sequence. We present a communication scheme with feedback that attains this rate universally without prior knowledge of the Noise Sequence.
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Universal Communication over Modulo-additive Channels with an Individual Noise Sequence
2010Co-Authors: Yuval Lomnitz, Meir FederAbstract:Which communication rates can be attained over an unknown channel where the relation between the input and output can be arbitrary? A channel where the output is any arbitrary (possibly stochastic) function of the input that may vary arbitrarily in time with no a-priori model? In this paper we provide an operational definition of a “capacity” (the maximal possible rate) for such an arbitrary infinite vector channel, which is similar in spirit to the finite-state compressibility of a Sequence defined by Lempel and Ziv. This capacity is the highest rate achieved by a designer that knows the particular relation that indeed exists between input and output for all times, yet is constrained to use a fixed finite-length block communication scheme (i.e., use the same scheme over each block). In the case where the relation between input and output is constrained to be “modulo additive” that is the channel generates the output Sequence by adding (modulo the channel alphabet) an arbitrary individual Sequence to the input Sequence, this capacity is upper bounded by 1 minus the finite state compressibilty of the Noise Sequence, multiplied by the logarithm of the alphabet size. We present a communication scheme with feedback that attains this rate universally without prior knowledge of the Noise Sequence.
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Communicating over modulo-additive channels with compressible individual Noise Sequence
2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, 2010Co-Authors: Yuval Lomnitz, Meir FederAbstract:We consider communication over a modulo additive channel where the Noise Sequence is an arbitrary Sequence unknown to the sender and receiver. We extend existing results by showing that using feedback and adapting the communication rate, one can communicate at a rate that is defined by the compression ratio of the individual Noise Sequence.
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Power Adaptive Feedback Communication over an Additive Individual Noise Sequence Channel
arXiv: Information Theory, 2009Co-Authors: Yuval Lomnitz, Meir FederAbstract:We consider a real-valued additive channel with an individual unknown Noise Sequence. We present a simple sequential communication scheme based on the celebrated Schalkwijk-Kailath scheme, which varies the transmit power according to the power of the Sequence, so that asymptotically the relation between the SNR and the rate matches the Gaussian channel capacity 1/2 log(1+SNR)for almost every Noise Sequence.
Alexander Lindner - One of the best experts on this subject based on the ideXlab platform.
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Strictly stationary solutions of multivariate ARMA equations with i.i.d. Noise
Annals of the Institute of Statistical Mathematics, 2012Co-Authors: Peter J Brockwell, Alexander Lindner, Bernd VollenbrökerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of multivariate ARMA equations with independent and identically distributed driving Noise. For general ARMA( p , q ) equations these conditions are expressed in terms of the coefficient polynomials of the defining equations and moments of the driving Noise Sequence, while for p = 1 an additional characterization is obtained in terms of the Jordan canonical decomposition of the autoregressive matrix, the moving average coefficient matrices and the Noise Sequence. No a priori assumptions are made on either the driving Noise Sequence or the coefficient matrices.
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Strictly stationary solutions of multivariate ARMA equations with i.i.d. Noise
Annals of the Institute of Statistical Mathematics, 2012Co-Authors: Peter J Brockwell, Alexander Lindner, Bernd VollenbrökerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of multivariate ARMA equations with independent and identically distributed driving Noise. For general ARMA(p, q) equations these conditions are expressed in terms of the coefficient polynomials of the defining equations and moments of the driving Noise Sequence, while for p = 1 an additional characterization is obtained in terms of the Jordan canonical decomposition of the autoregressive matrix, the moving average coefficient matrices and the Noise Sequence. No a priori assumptions are made on either the driving Noise Sequence or the coefficient matrices. Copyright The Institute of Statistical Mathematics, Tokyo 2012
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Strictly stationary solutions of multivariate ARMA equations with i.i.d. Noise
arXiv: Statistics Theory, 2011Co-Authors: Peter J Brockwell, Alexander Lindner, Bernd VollenbroekerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of multivariate ARMA equations with independent and identically distributed Noise. For general ARMA$(p,q)$ equations these conditions are expressed in terms of the characteristic polynomials of the defining equations and moments of the driving Noise Sequence, while for $p=1$ an additional characterization is obtained in terms of the Jordan canonical decomposition of the autoregressive matrix, the moving average coefficient matrices and the Noise Sequence. No a priori assumptions are made on either the driving Noise Sequence or the coefficient matrices.
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strictly stationary solutions of autoregressive moving average equations
Biometrika, 2010Co-Authors: Peter J Brockwell, Alexander LindnerAbstract:Necessary and sufficient conditions for the existence of a strictly stationary solution of the equations defining an autoregressive moving average process driven by an independent and identically distributed Noise Sequence are determined. No moment assumptions on the driving Noise Sequence are made. Copyright 2010, Oxford University Press.
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Extremes of autoregressive threshold processes
Advances in Applied Probability, 2009Co-Authors: Claudia Brachner, Vicky Fasen, Alexander LindnerAbstract:In this paper we study the tail and the extremal behaviors of stationary solutions of threshold autoregressive (TAR) models. It is shown that a regularly varying Noise Sequence leads in general to only an O-regularly varying tail of the stationary solution. Under further conditions on the partition, it is shown however that TAR(S,1) models of order 1 with S regimes have regularly varying tails, provided that the Noise Sequence is regularly varying. In these cases, the finite-dimensional distribution of the stationary solution is even multivariate regularly varying and its extremal behavior is studied via point process convergence. In particular, a TAR model with regularly varying Noise can exhibit extremal clusters. This is in contrast to TAR models with Noise in the maximum domain of attraction of the Gumbel distribution and which is either subexponential or in ℒ(γ) with γ > 0. In this case it turns out that the tail of the stationary solution behaves like a constant times that of the Noise Sequence, regardless of the order and the specific partition of the TAR model, and that the process cannot exhibit clusters on high levels.
Bernd Vollenbröker - One of the best experts on this subject based on the ideXlab platform.
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Strictly stationary solutions of multivariate ARMA equations with i.i.d. Noise
Annals of the Institute of Statistical Mathematics, 2012Co-Authors: Peter J Brockwell, Alexander Lindner, Bernd VollenbrökerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of multivariate ARMA equations with independent and identically distributed driving Noise. For general ARMA( p , q ) equations these conditions are expressed in terms of the coefficient polynomials of the defining equations and moments of the driving Noise Sequence, while for p = 1 an additional characterization is obtained in terms of the Jordan canonical decomposition of the autoregressive matrix, the moving average coefficient matrices and the Noise Sequence. No a priori assumptions are made on either the driving Noise Sequence or the coefficient matrices.
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Strictly stationary solutions of ARMA equations with fractional Noise
Journal of Time Series Analysis, 2012Co-Authors: Bernd VollenbrökerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of ARMA equations with fractional Noise. Here, the underlying Noise Sequence of the fractional Noise is assumed to be i.i.d. but no a priori moment assumptions are made. We also characterize for which i.i.d. driving Noise Sequences the series defining fractional Noise converges almost surely. In the proofs, we use growth estimates for the moments of random walks developed by Manstavicius (1982) and techniques related to those of Brockwell and Lindner (2010) for the existence of strictly stationary ARMA processes with i.i.d. Noise.
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Strictly stationary solutions of multivariate ARMA equations with i.i.d. Noise
Annals of the Institute of Statistical Mathematics, 2012Co-Authors: Peter J Brockwell, Alexander Lindner, Bernd VollenbrökerAbstract:We obtain necessary and sufficient conditions for the existence of strictly stationary solutions of multivariate ARMA equations with independent and identically distributed driving Noise. For general ARMA(p, q) equations these conditions are expressed in terms of the coefficient polynomials of the defining equations and moments of the driving Noise Sequence, while for p = 1 an additional characterization is obtained in terms of the Jordan canonical decomposition of the autoregressive matrix, the moving average coefficient matrices and the Noise Sequence. No a priori assumptions are made on either the driving Noise Sequence or the coefficient matrices. Copyright The Institute of Statistical Mathematics, Tokyo 2012
Satoshi Nakamura - One of the best experts on this subject based on the ideXlab platform.
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A Non-stationary Noise Suppression Method Based on Particle Filtering and Polyak Averaging
IEICE Transactions on Information and Systems, 2006Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses a speech recognition problem in non-stationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary Noise environments a Noise compensation method with stationary Noise assumptions.
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Particle filtering and Polyak averaging-based non-stationary Noise tracking for ASR in Noise
IEEE Workshop on Automatic Speech Recognition and Understanding 2005., 2005Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses a speech recognition problem in non-stationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary Noise environments a Noise compensation method with stationary Noise assumptions
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ICASSP (1) - Particle filter based non-stationary Noise tracking for robust speech recognition
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses the main speech recognition problem in nonstationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated through a sequential importance sampling step, then a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is applied to MMSE-based clean speech estimation method. The evaluations were conducted on speech recognition in highly nonstationary Noise environments. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in non-stationary Noise environments over Noise compensation with stationary Noise assumptions.
M. Fujimoto - One of the best experts on this subject based on the ideXlab platform.
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A Non-stationary Noise Suppression Method Based on Particle Filtering and Polyak Averaging
IEICE Transactions on Information and Systems, 2006Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses a speech recognition problem in non-stationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary Noise environments a Noise compensation method with stationary Noise assumptions.
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Particle filtering and Polyak averaging-based non-stationary Noise tracking for ASR in Noise
IEEE Workshop on Automatic Speech Recognition and Understanding 2005., 2005Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses a speech recognition problem in non-stationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated in three stages: a sequential importance sampling step, a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is used in the MMSE-based clean speech estimation. We also introduce Polyak averaging and feedback into a state transition process for particle filtering. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in the results of non-stationary Noise environments a Noise compensation method with stationary Noise assumptions
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ICASSP (1) - Particle filter based non-stationary Noise tracking for robust speech recognition
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1Co-Authors: M. Fujimoto, Satoshi NakamuraAbstract:This paper addresses the main speech recognition problem in nonstationary Noise environments: the estimation of Noise Sequences. To solve this problem, we present a particle filter-based sequential Noise estimation method for front-end processing of speech recognition in Noise. In the proposed method, a Noise Sequence is estimated through a sequential importance sampling step, then a residual resampling step, and finally a Markov chain Monte Carlo step with Metropolis-Hastings sampling. The estimated Noise Sequence is applied to MMSE-based clean speech estimation method. The evaluations were conducted on speech recognition in highly nonstationary Noise environments. In the evaluation results, we observed that the proposed method improves speech recognition accuracy in non-stationary Noise environments over Noise compensation with stationary Noise assumptions.
