Dereverberation is a growing area of research with many new algorithms appearing in the literature However, there are still no unanimously accepted tools for evaluation of these algorithms In this paper, we introduce the Multichannel Acoustic Reverberation Database at York (MARDY) containing real measured multichannel room impulse responses We demonstrate its use for the evaluation of dereverberation algorithms using three recent multichannel methods Furthermore, psychoacoustic issues regarding the performance evaluation of dereverberation algorithms are discussed

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Evaluation of speech dereverberation algorithms using the mardy database

Soft-input soft-output (SISO) detection algorithms form the basis for iterative decoding. The associated computational complexity often poses significant challenges for practical receiver implementations, in particular in the context of multiple- input multiple-output wireless systems. In this paper, we present a low-complexity SISO sphere decoder which is based on the single tree search paradigm, proposed originally for soft-output detection in Studer et al., IEEE J-SAC, 2008. The algorithm incorporates clipping of the extrinsic log-likelihood ratios in the tree search, which not only results in significant complexity savings, but also allows to cover a large performance/complexity trade-off region by adjusting a single parameter.

Soft-input soft-output sphere decoding

This paper introduces a general theory of extended Hamiltonian (second-order) learning on Riemannian manifolds, as an instance of learning by constrained criterion optimization. The dynamical learning equations are derived within the general framework of extended-Hamiltonian stationary-action principle and are expressed in a coordinate-free fashion. A theoretical analysis is carried out in order to compare the features of the dynamical learning theory with the features exhibited by the gradient-based ones. In particular, gradient-based learning is shown to be an instance of dynamical learning, and the classical gradient-based learning modified by a “momentum” term is shown to resemble discrete-time dynamical learning. Moreover, the convergence features of gradient-based and dynamical learning are compared on a theoretical basis. This paper discusses cases of learning by dynamical systems on manifolds of interest in the scientific literature, namely, the Stiefel manifold, the special orthogonal group, the Grassmann manifold, the group of symmetric positive definite matrices, the generalized flag manifold, and the real symplectic group of matrices.

/pdf/extended-hamiltonian-learning-on-riemannian-manifolds-2oy2tm8yly.pdf

Extended Hamiltonian Learning on Riemannian Manifolds: Theoretical Aspects

This paper provides a new iterative solution to the problem of the joint estimation of phase noise and residual carrier frequency offset in an orthogonal frequency division multiplexing transmission over a multipath fading channel, in the presence of known data and channel impulse response. The proposed technique results from the application of the sum-product algorithm to a factor graph representing the joint a posteriori probability density function of the carrier frequency offset and the phase noise vector affecting a given symbol. The resulting algorithm is shown to be exploited in a coherent receiver for both data-aided training and decision-directed tracking. Its performance is assessed by computer simulations and is compared with that provided by other strategies.

A Factor Graph Approach to the Iterative Detection of OFDM Signals in the Presence of Carrier Frequency Offset and Phase Noise

In this paper we address the problem of channel equalization and phase noise suppression in orthogonal frequency division multiplexing (OFDM) systems. For OFDM systems, random phase noise introduced by the local oscillator causes two effects: the common phase error (CPE), and the intercarrier interference (ICI). The performance of coherent OFDM systems greatly depends on the ability to accurately estimate the effective dynamic channel, i.e. the combined effect of the CPE and the time-varying frequency selective channel. The proposed approach uses a pilot tone aided particle filter to track/estimate the effective dynamic channel in the time domain and equalizes in the frequency domain. The particle filter is efficiently implemented by combining sequential importance sampling, principles of Rao-Blackwellization, and strategies stemming from the auxiliary particle filter. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.

Channel equalization and phase noise suppression for OFDM systems in a time-varying frequency selective channel using particle filtering

A blind deconvolution algorithm is presented to address the problem of the dereverberation of speech. A Bayesian algorithm is developed for estimating the source, and the problem of ill-conditioning due to long tails of an acoustic impulse response (AIR) is avoided by marginalizing out the unknown channel parameters. The initial samples of the MAP estimate are determined using a stochastic MCMC (Markov chain Monte Carlo) technique, and these estimates are then used in a sequential procedure for estimating the remaining signal. A filterbank implementation is used to reduce the large deconvolution problem into several smaller independent problems. Simulation results are presented to demonstrate the performance of the algorithm applied to the dereverberation of speech.

J.R. Reilly

Papers

Channel equalization and phase noise suppression for OFDM systems in a time-varying frequency selective channel using particle filtering

Blind deconvolution using Bayesian methods with application to the dereverberation of speech