6,122 research outputs found
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
Sensor array signal processing : two decades later
Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg
Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference
This work addresses the problem of direction-of-arrival (DOA) estimation in
the presence of non-Gaussian, heavy-tailed, and spatially-colored interference.
Conventionally, the interference is considered to be Gaussian-distributed and
spatially white. However, in practice, this assumption is not guaranteed, which
results in degraded DOA estimation performance. Maximum likelihood DOA
estimation in the presence of non-Gaussian and spatially colored interference
is computationally complex and not practical. Therefore, this work proposes a
neural network (NN) based DOA estimation approach for spatial spectrum
estimation in multi-source scenarios with a-priori unknown number of sources in
the presence of non-Gaussian spatially-colored interference. The proposed
approach utilizes a single NN instance for simultaneous source enumeration and
DOA estimation. It is shown via simulations that the proposed approach
significantly outperforms conventional and NN-based approaches in terms of
probability of resolution, estimation accuracy, and source enumeration accuracy
in conditions of low SIR, small sample support, and when the angular separation
between the source DOAs and the spatially-colored interference is small.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System
Applications of Bayesian model selection to cosmological parameters
Bayesian model selection is a tool to decide whether the introduction of a
new parameter is warranted by data. I argue that the usual sampling statistic
significance tests for a null hypothesis can be misleading, since they do not
take into account the information gained through the data, when updating the
prior distribution to the posterior. On the contrary, Bayesian model selection
offers a quantitative implementation of Occam's razor.
I introduce the Savage-Dickey density ratio, a computationally quick method
to determine the Bayes factor of two nested models and hence perform model
selection. As an illustration, I consider three key parameters for our
understanding of the cosmological concordance model. By using WMAP 3-year data
complemented by other cosmological measurements, I show that a non-scale
invariant spectral index of perturbations is favoured for any sensible choice
of prior. It is also found that a flat Universe is favoured with odds of 29:1
over non--flat models, and that there is strong evidence against a CDM
isocurvature component to the initial conditions which is totally
(anti)correlated with the adiabatic mode (odds of about 2000:1), but that this
is strongly dependent on the prior adopted.
These results are contrasted with the analysis of WMAP 1-year data, which
were not informative enough to allow a conclusion as to the status of the
spectral index. In a companion paper, a new technique to forecast the Bayes
factor of a future observation is presented.Comment: v2 to v3: minor changes, matches accepted version by MNRAS. v1 to v2:
major revision. New results using WMAP 3-yr data, scale-invariant spectrum
now disfavoured with moderate evidence. New benchmark test for the accuracy
of the method. Bayes factor forecast methodology (PPOD, formerly called ExPO)
expanded and now presented in a companion paper (astro-ph/0703063
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