12,444 research outputs found
Robust equalization of multichannel acoustic systems
In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved
by equalizing the channels from the source to microphones. Equalization systems can
be computed using estimates of multichannel acoustic impulse responses. However,
the estimates obtained from system identification always include errors; the fact that
an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this
thesis is to propose and investigate robust equalization methods for multichannel
acoustic systems in the presence of system identification errors.
Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems
obtained from these methods are very sensitive to system identification errors. A
study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least-
squares method is proposed. Channel shortening in connection with the multiple-
input/output inverse theorem and the relaxed multichannel least-squares method is
discussed.
Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is
proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization
method exploring the use of error models is presented, which incorporates system
identification error models in the weighted multichannel least-squares formulation
Comparison of wind speed measurements over the oceans with the Special Sensor Microwave/Imager and the Geosat altimeter
In order to compare wind speed estimates from the Geosat altimeter and the Special Sensor Microwave/Imager (SSM/I), 25 colocated passes, within 2 hours of each other, were selected and the SSM/I estimates of wind speed and atmospheric parameters extracted along the Geosat track. Both instruments and their algorithms are described. A statistical comparison of wind speed estimates is presented and the effects of the atmospheric parameters from Geosat are analyzed. Quasi-simultaneous measurements by Geosat and SSM/I, along a Geosat track in the North-East Pacific, are also presented
A Generalized Algorithm for Blind Channel Identification with Linear Redundant Precoders
It is well known that redundant filter bank precoders can be used for blind identification as well as equalization of FIR channels. Several algorithms have been proposed in the literature exploiting trailing zeros in the transmitter. In this paper we propose a generalized algorithm of which the previous algorithms are special cases. By carefully choosing system parameters, we can jointly optimize the system performance and computational complexity. Both time domain and frequency domain approaches of channel identification algorithms are proposed. Simulation results show that the proposed algorithm outperforms the previous ones when the parameters are optimally chosen, especially in time-varying channel environments. A new concept of generalized signal richness for vector signals is introduced of which several properties are studied
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Towards the P-wave nucleon-pion scattering amplitude in the channel
We use lattice QCD and the L\"uscher method to study elastic pion-nucleon
scattering in the isospin channel, which couples to the
resonance. Our flavor lattice setup features a pion
mass of MeV, such that the strong decay channel is close to the threshold. We present our method for
constructing the required lattice correlation functions from single- and
two-hadron interpolating fields and their projection to irreducible
representations of the relevant symmetry group of the lattice. We show
preliminary results for the energy spectra in selected moving frames and
irreducible representations, and extract the scattering phase shifts. Using a
Breit-Wigner fit, we also determine the resonance mass and the
coupling.Comment: 14 pages, 7 figures, Proceedings of the 36th Annual International
Symposium on Lattice Field Theory (Lattice 2018), 22-28 July 2018, Michigan
State University, East Lansing, Michigan US
Subspace Methods for Joint Sparse Recovery
We propose robust and efficient algorithms for the joint sparse recovery
problem in compressed sensing, which simultaneously recover the supports of
jointly sparse signals from their multiple measurement vectors obtained through
a common sensing matrix. In a favorable situation, the unknown matrix, which
consists of the jointly sparse signals, has linearly independent nonzero rows.
In this case, the MUSIC (MUltiple SIgnal Classification) algorithm, originally
proposed by Schmidt for the direction of arrival problem in sensor array
processing and later proposed and analyzed for joint sparse recovery by Feng
and Bresler, provides a guarantee with the minimum number of measurements. We
focus instead on the unfavorable but practically significant case of
rank-defect or ill-conditioning. This situation arises with limited number of
measurement vectors, or with highly correlated signal components. In this case
MUSIC fails, and in practice none of the existing methods can consistently
approach the fundamental limit. We propose subspace-augmented MUSIC (SA-MUSIC),
which improves on MUSIC so that the support is reliably recovered under such
unfavorable conditions. Combined with subspace-based greedy algorithms also
proposed and analyzed in this paper, SA-MUSIC provides a computationally
efficient algorithm with a performance guarantee. The performance guarantees
are given in terms of a version of restricted isometry property. In particular,
we also present a non-asymptotic perturbation analysis of the signal subspace
estimation that has been missing in the previous study of MUSIC.Comment: submitted to IEEE transactions on Information Theory, revised versio
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
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