543 research outputs found
ODAS: Open embeddeD Audition System
Artificial audition aims at providing hearing capabilities to machines,
computers and robots. Existing frameworks in robot audition offer interesting
sound source localization, tracking and separation performance, but involve a
significant amount of computations that limit their use on robots with embedded
computing capabilities. This paper presents ODAS, the Open embeddeD Audition
System framework, which includes strategies to reduce the computational load
and perform robot audition tasks on low-cost embedded computing systems. It
presents key features of ODAS, along with cases illustrating its uses in
different robots and artificial audition applications
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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