285 research outputs found
PSD Estimation and Source Separation in a Noisy Reverberant Environment using a Spherical Microphone Array
In this paper, we propose an efficient technique for estimating individual
power spectral density (PSD) components, i.e., PSD of each desired sound source
as well as of noise and reverberation, in a multi-source reverberant sound
scene with coherent background noise. We formulate the problem in the spherical
harmonics domain to take the advantage of the inherent orthogonality of the
spherical harmonics basis functions and extract the PSD components from the
cross-correlation between the different sound field modes. We also investigate
an implementation issue that occurs at the nulls of the Bessel functions and
offer an engineering solution. The performance evaluation takes place in a
practical environment with a commercial microphone array in order to measure
the robustness of the proposed algorithm against all the deviations incurred in
practice. We also exhibit an application of the proposed PSD estimator through
a source septation algorithm and compare the performance with a contemporary
method in terms of different objective measures
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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